Contemporary Role and Applications of Artificial Intelligence in Dentistry

Talal Bonny; Wafaa Al Nassan; Khaled Obaideen; Maryam Nooman Al Mallahi; Yara Mohammad; Hatem M. El-damanhoury

doi:10.12688/f1000research.140204.1

Home Browse Contemporary Role and Applications of Artificial Intelligence in Dentistry

ALL Metrics

-

Views

-

Downloads

Get PDF

Get XML

Export

▬

✚

Review

Contemporary Role and Applications of Artificial Intelligence in Dentistry

[version 1; peer review: 1 approved, 2 approved with reservations]

Talal Bonny¹, Wafaa Al Nassan¹, Khaled Obaideen², Maryam Nooman Al Mallahi³, Yara Mohammad⁴, Hatem M. El-damanhoury ⁵

Talal Bonny¹, Wafaa Al Nassan¹, [...] Khaled Obaideen², Maryam Nooman Al Mallahi³, Yara Mohammad⁴, Hatem M. El-damanhoury ⁵

PUBLISHED 20 Sep 2023

Author details Author details

¹ Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
² Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
³ Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
⁴ ‎College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
⁵ ‎Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates

Talal Bonny
Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Wafaa Al Nassan
Roles: Methodology, Resources, Writing – Original Draft Preparation, Writing – Review & Editing

Khaled Obaideen
Roles: Methodology, Resources, Visualization, Writing – Original Draft Preparation

Maryam Nooman Al Mallahi
Roles: Investigation, Resources, Writing – Original Draft Preparation

Yara Mohammad
Roles: Resources, Writing – Original Draft Preparation

Hatem M. El-damanhoury
Roles: Investigation, Project Administration, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Artificial Intelligence and Machine Learning gateway.

This article is included in the AI in Medicine and Healthcare collection.

Abstract

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Keywords

Artificial Intelligence, Digital Dentistry, Dentistry, Deep Learning, Artificial Neural Networks, ‎Dental Specialty

Corresponding author: Hatem M. El-damanhoury

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Bonny T et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Bonny T, Al Nassan W, Obaideen K et al. Contemporary Role and Applications of Artificial Intelligence in Dentistry [version 1; peer review: 1 approved, 2 approved with reservations]. F1000Research 2023, 12:1179 (https://doi.org/10.12688/f1000research.140204.1) First published: 20 Sep 2023, 12:1179 (https://doi.org/10.12688/f1000research.140204.1) Latest published: 20 Sep 2023, 12:1179 (https://doi.org/10.12688/f1000research.140204.1)

Introduction

Artificial intelligence (AI) is the process of training a computer in such a way that it starts thinking like human beings.¹ With the advancement in technology in AI, the idea behind the machine that performs tasks for human beings is becoming old-fashioned. Devices and systems that can perform basic calculations and fundamental tasks or detect the text with various recognition applications are now considered standard computer applications and does not lie under AI. This globally recognized technique constantly improves many sectors, including medical, engineering,²^–⁶ energy,⁷^,⁸ mechanical,⁹ computer science,¹⁰^,¹¹ psychology,¹² and other disciplines.¹³^–¹⁹ This constant improvement in AI makes it an important constituent of almost every field. Along the same line, AI is one of the enablers of achieving the United Nations Sustainable Development Goals (SDGs).²⁰ More specifically, AI could accelerate the achievement of SDG 3: Good Health and Well-being.²¹^,²²

In dentistry, AI is becoming an important constituent owing to its use in improving the diagnostic facility to provide excellent patient care and the ability to get excellent results.²³^–²⁶ Dentists are capable enough with their training to analyze and suggest the best possible treatment and provide the best clinical decision based on their experiences.²⁷^,²⁸ However, sometimes, dentists do not have sufficient information and knowledge to predict the best clinical practice.²⁹ This may result in poor clinical decision-making and bad effects on human well-being.³⁰ However, the dentist can perform better using AI systems by making accurate decisions.³¹ They can rely on computer and AI software to make clinical decisions.³²^,³³ Several papers intend to explore and review the role of AI in dentistry.³⁴^,³⁵ However, most of these papers were very technical or focused on the technical perspective of AI's application in dentistry, making them quite difficult for dental practitioners.

This paper attempts to review the solutions used in digital dentistry that engineers have developed from various fields of knowledge. As a result, this knowledge is critical for dentists, surgeons, clinicians, and laboratory technicians. Moreover, most of the previous papers did not explore the role of AI in different dental specialties; diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general, so this paper intends to fill this gap by reviewing the application of AI in dentistry using simplified terms and showing the role of AI in different dentistry specializations. As a result, this paper focuses on answering the following research questions:

• What are the trends, applications, and innovations of AI in dentistry and digital dentistry that are expected to influence the direction of dental research in the coming years?
• What are the strengths and weaknesses of AI in dentistry?
• What are the challenges of using AI in dentistry?

The paper is structured as follows. The next section reviews artificial intelligence and its application in healthcare. The application of AI in dentistry and digital dentistry is then described. The strengths and weaknesses of AI in dentistry are presented in the section after. Then we present the main challenges of using AI in dentistry. Finally, the conclusion is given in the final section.

Artificial Intelligence (AI) for Healthcare

The science of AI contains different branches, such as machine learning (ML) and deep learning (DL). ML is a system that can be trained by different problem-solving training and models to gain the ability to automate the process of solving tasks.³⁶ DL is a part of ML in which the learning module is based on artificial neural networks.³⁷ DL provides an excellent ability to beat state-of-the-art techniques in its application to perform various tasks and analyze and evaluate data from various sources, including audio,³⁸^,³⁹ sensors,⁴⁰ and visual data.⁴¹ On the other hand, there are several types of artificial intelligence. Under two main categories based on their functionalities and capabilities, as shown in Figure 1.

Figure 1. Artificial Intelligence types.

Since its inception, AI has significantly improved our daily lives and activities in various ways. Emerging fields, including surgery,⁴² automatic disease diagnosis,⁴³ and the recently established personalized medicine, can benefit from AI support.⁴⁴ Figure 2 demonstrates the key aspects of artificial intelligence, ranging from ML, Neural Networks (NN), and DL.

Figure 2. Key aspects of artificial intelligence.

Moreover, AI models were implemented to central processing unit (CPU) and graphics processing unit (GPU) architectures, so detection\recognition-based algorithms can achieve practical needs. However, recent AI algorithms, especially deep learning models, demonstrated efficiency in solving many problems. Those algorithms demand an intensive CPU and memory, which prevents standard CPUs from achieving the desired performance levels.⁴⁵ As a result, hardware processors, specifically field-programmable gate arrays (FPGAs) platforms, have been used to effectively maximize those models' performance.⁴⁶^–⁴⁸ FPGA is an integrated circuit customized for a specific application where the configuring process is applied after manufacturing.⁴⁹ Consequently, FPGA is the perfect candidate for implementation from various perspectives, including but not limited to performance for parallel tasks, cost-effectiveness, prototyping, and real-time applications.⁵⁰^,⁵¹

Application of AI in Dentistry

Artificial intelligence has a large number of advantages in various businesses as well as various domains. In healthcare, AI has been getting attention in the medical and pharmaceutical fields by providing self-driven operating applications. AI applications in the healthcare domain include Robotic systems, Diagnostic Aids, and Drug Discovery.⁵²^,⁵³

AI emerged in the healthcare field to "train" computer programs to reach highly intelligent capabilities. AI, a superior diagnostic aid that minimizes daily practice mistakes, might enhance patient care.⁵⁴ The interpretation of medical images has progressed from expert systems through atlas-based models toward the potential of deep learning. The huge data from digital radiographs may be utilized since it has much potential for improving radiology diagnosis with AI. Through automated data mining, AI with DL might help medical radiology.⁵⁵ New information will be found with only a little human understanding.⁵⁶^–⁵⁹

The identification and management of systemic conditions during dental treatment has always been a challenge to the dentists. AI can examine electronic medical records and scientific datasets practically and efficiently. With the advent of huge scientific data, the diagnosis of human congenital abnormalities has improved. In medical research, the support vector machine (SVM), a machine-learning model, has become a typical way of analysis. SVM may be used to classify complex situations like traumatic brain damage.⁶⁰^,⁶¹ DL may play a crucial role in advancing cardiovascular medicine due to the requirement for precision in diagnosis and therapy.⁶² With the adoption of deep learning, it will be feasible to identify numerous cardiovascular disorders with high-precision diagnosis.⁶³ Wearable technology combined with intelligence applications can anticipate a patient's life-threatening crisis such as a stroke, allowing physicians to deliver effective, early therapies.⁵⁴ Using AI to examine viruses has boosted transitional studies in viral immune monitoring.⁶⁴

AI can help fill knowledge gaps while also lowering costs and increasing benefits. AI is commonly utilized in disease management to examine treatment outcomes and provide precision medicine. These cutting-edge machine-learning algorithms are powerful analytic tools that assist clinicians in understanding and analyzing moods.

Traditional components of dentistry have been modernized by using AI. In dentistry, AI-based diagnostic systems and patient data are widely used. They are mostly decision systems based on clinical knowledge, which leads to helping and advising specialists in making better judgments. This highly efficient technology has been employed to analyze treatment planning, diagnosis, and prognosis prediction. Because of their usefulness in offering explanations and logic, these systems are in high demand. AI has transformed dentistry, resulting in reducing the work of a dentist.⁶⁵ AI-based technology in dentistry is mainly aimed at providing expert assistance to healthcare providers.⁶⁶^–⁶⁸ Any computer software created to assist health professionals in making clinical choices and dealing with medical knowledge essential for understanding such data is referred to as a clinical decision support system.³² Figure 3 displays the structure and major dental applications of AI.

Figure 3. Summary of the hierarchy and major dental applications of AI.

After reviewing various scientific journals, key information such as applications, AI techniques, and field of research was investigated and categorized, as shown in Figure 4.

Figure 4. The categories of AI applications in dentistry.

State-of-the-art AI technology in Dentistry

This section describes the state of the art of AI technology in the dentistry field. AI provides essential help to automate different activities, such as the assessment of a radiographic image to detect a dental disease faster and more accurately than other activities. It has been shown how appropriately trained algorithms can be extremely successful for the diagnostician and how AI can determine the direction of public health and clinical services.

ANNs

Artificial neural networks (ANN’s) are systems based on algorithms, learning on a neural network with biological functionality. Such systems address various challenges, including supervised, unsupervised, and reinforcement learning. An ANN's fundamental structure is a series of data layers interconnected by operational neurons. The most promising area of study in this discipline is undoubtedly DL applications in dentistry. Because these approaches allow discovering particular arrangements from massive data sets of pictures, they can help build a high-performance system with excellent decision-making capabilities.⁶⁹

Although it may not appear that advances in AI have had a significant impact on dentistry, various areas, including automated detection of oral diseases, enhancement and analysis of dental x-rays and images, are some of the benefits of AI in dentistry. On the robotics front, various advancements are allowing robotic assistance in dentistry.⁷⁰

Several variables have played a role in the latest AI revolution in biomedicine. For starters, data collection has grown exponentially over the past few decades. However, data alone is insufficient. New sophisticated AI algorithms have permitted a complete and helpful extraction of information from acquired data because of improvements in high-performance computing (HPC). This information extraction technique is called ML, the data-driven component of AI that seeks to enable machines (algorithms implemented in computer systems) to learn about a specific topic from a specific dataset.⁷¹ This sort of information extraction is often carried out utilizing supervised learning approaches, which have proven to be quite effective in solving many issues. Supervised learning aims to train a function that converts an input sample to the desired output using a dataset of input-output pairings. After learning this function using the training dataset, new predictions can be formed over incoming samples.⁷²

These technologies have also seen substantial medical adoption, namely in computer vision. A variety of factors has been recognized as driving this adoption. Diagnostic imaging is critical in many healthcare disciplines,⁷³ and AI is particularly well suited to overcoming the diversity in individual subjective inspection and increasing the effectiveness of therapy while cutting costs by removing mundane activities. Digital health data are being collected everywhere, and while these data are currently somewhat heterogeneous, businesses are progressively aiming to deliver cleansed, curated, and organized data.⁷⁴

The convolutional neural network (CNN) is the ANN's most often utilized subclasses in medicine and dentistry.⁷⁵ A CNN employs a unique neuron connection architecture and the mathematical function convolution to handle digital data such as sound, pictures, and videos. CNNs evaluate a larger picture or signal by scanning a small neighborhood of inputs simultaneously, from left to right and top to bottom, using a sliding window. They are the most often used image recognition method because they are particularly well matched to picture categorization.⁷⁶

Augmented Reality and Virtual Reality

Dental care is a one-of-a-kind career in the medical field, and it is incredibly required as it is a necessity hence the incorporation of a vast quantity of data and the development of clinical skills.⁷⁷ A technology that superimposes a computer-generated picture over a user's perspective of the actual world, producing a composite vision, according to the definition of augmented reality.⁷⁸ The development of augmented reality has generated appealing prosthetics resulting in better and improved overall patient experiences. The patient can try on the augmented reality that can be altered until they are satisfied using AI algorithms and augmented reality. The final prosthesis is produced precisely to these specifications.⁷⁹^,⁸⁰

Irrespective of augmented reality, virtual reality is a simulation based on a 3D picture generated by the computer, which can interact with the real and physical world and electrical and electronic equipment. In combination with virtual reality, AI systems have been utilized to alleviate dental concerns and as a strong tool for treating a patient with pain that cannot be cured by medicine.⁸¹

The field of intelligent teaching organizations has advanced greatly since its inception in the 1980s. To create scenarios that simulate clinical work on patients while removing the risks associated with teaching a live patient, augmented reality and virtual reality are both extensively used in dentistry education.⁸² The preclinical virtual patient has greatly improved the quality of feedback provided since AI was recently included in intelligent teaching systems, namely the Unified Medical Language System (UMLS). In the interactive interphase, students can evaluate their work and compare it to the ideal, resulting in high-quality training settings. Studies has highlighted the importance of these systems and reported that students achieve competency-based skill levels faster than those using standard simulator modules.⁸³^,⁸⁴

Data Mining

While ML makes predictions based on an analytical approach to available data, data mining is concerned with discovering causal links and similarities in current data. Data mining of digitized dental data allows for the analysis of variance across dentists when detecting and predicting dental caries, dental age estimation.³³^,⁸⁵^–⁸⁷

Applications in the Various Fields of Dentistry

Dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral and maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, and prosthodontics are dental specialties recognized by the Canadian Dental Association as shown in Figure 5.⁸⁸

Figure 5. Dental Specialties based on Canadian Dental Association.

Virtual dental assistants working on AI are now working for various domains in the market. Like virtual assistants, these designed systems can perform specific tasks in dental clinics more precisely and accurately, using less power, resulting in less error than human beings.⁸⁰

Some of the virtual assistant work in the field of dentistry includes,

▪ Offering the patient a convenient appointment time reduces the potential for commotion and activity arising due to the large number of patients coming in a day.⁸⁹
▪ Informing the dentist and the patients regarding the upcoming checks when hereditary or lifestyle factors suggest a higher vulnerability to oral problems. (e.g., diabetic patient's periodontal screening and users of smoked and smokeless tobacco regularly will be tested for oral cancer screening).⁹⁰
▪ Taking care of paperwork and insurance.
▪ Helping in better diagnosis and planning after the patient's disease diagnosis is carried out.
▪ Provide the dentist with information about the allergies and diseases the patient already has before the patient visit.
▪ Providing the dentist with the associate medical background. This will help the doctor finalize the plan regarding the patient's diagnosis. This also helps in planning surgery for the patient.³³
▪ Offering telephone-based services on time in the cases when the dentist cannot reach the hospital and urgent medical care is required.⁹¹

Figure 6 demonstrates a comprehensive dental care system for the future with AI. Aside from this, AI-based software allows us to develop a comprehensive data history for each patient, which may be incredibly thorough and accessible.²⁷^,⁹² The AI software can recognize the voice, which will help the dentist improve their performance. AI software can perform documentation and display all the data relevant to the dentist at a rather efficient and faster speed than a human equivalent. (For example, gathering all relevant patient records on their dental visits, supplementary oral pictures, x-rays, and diagnostic graphs related to their disease). Owing to these exceptional learning abilities, it can be trained to execute various tasks. Such devices can be used alongside dental x-rays and CT-scans to detect changes in human teeth from their normal teeth that the dentist's eyes may not have noticed.²⁷^,⁹³

Figure 6. The comprehensive dental care system of the future with AI.

It can be divided into pre-appointment, inter-appointment, and post-appointment AI systems. These three types of AI gather data that will be used to enhance patient care.

This technique may also be used to reach orthodontic diagnosis by analyzing the cephalometric radiographs and detecting dental and skeletal abnormalities.⁹⁴ Simply by successfully obtaining the patient's data and synchronizing it with the decision system already in place for the patient in the dental labs, this is accomplished.⁹⁵ In pathology, scanning numerous sections to find minute details that aid in diagnosis and clinical decision-making is possible. The program can do different analyses of images and radiographs in orthodontics to aid in diagnosis and treatment planning.⁹⁶ Taking a dental impression might soon be obsolete due to advancements in intra-oral scanners and cameras. These digital impressions minimize all laboratory operations, which sharply lowers the frequency of errors and is quicker and more accurate.

Using AI, the computer can notify the dentist throughout the process of creating a digital imprint and assist in making an excellent impression.⁹⁷ Taking a dental impression may soon be a thing of the past thanks to advancements in intra-oral scanners and cameras. In addition to being quicker and more accurate, these digital images also do away with all laboratory procedures, which greatly reduces the possibility of mistakes. Using AI, the computer can guide the dentist as they create a digital impression, optimize the final scan by eliminating the unwanted frames during scanning, automatically detect the margins of the preparations and develop a restoration design that imitate the anatomy of the lost tooth structure.⁹⁸ Computer Aided Design (CAD) Computer Aided Manufacturing (CAM) techniques, like subtractive milling, and additive manufacturing techniques, like 3D printing, are currently used to create prostheses. It has replaced the labor-intensive and challenging traditional casting process while dramatically lowering human error in the finished prosthesis. These technologies can also be employed to create precise orthodontic appliances.⁹⁸

AI software has aided in pre-surgical planning procedures to the smallest detail in oral surgery and implantology. The introduction of robotic surgery has made one of the most significant uses of AI in oral and maxillofacial surgery. Simulating human body motion and intellect is critical in robotics science.⁹⁹ However, the discipline of surgery has been revolutionized by artificial intelligence. The number of robotic surgeons doing semi-automated surgical procedures under the supervision of a skilled surgeon has increased significantly in recent years. Last but not least, one of the most cutting-edge uses of AI is found in the field of "bioprinting," which allows living tissue and even organs to be built into successively thin layers of cells. This technology could one day reconstruct oral hard and soft tissues lost due to pathological or unintentional causes.¹⁰⁰

Dental Public Health

Artificial intelligence (AI) can be used in Dental Public Health, which involves diagnosing, preventing, and controlling dental diseases through research, education, and group dental care programs.

As an example, an AI-assisted diagnosis method is developed by employing Faster-Recurrent Convolutional Neural Network (RCNN) to predict the number and locations of caries lesions based on periapical films, which in the end, will help doctors diagnose the diseases with higher efficiency and precision.¹⁰¹

Endodontics

Although root canal configuration in a group of mandibular molars may seem similar, numerous unusual deviations may exist.⁷³ Cone-beam computed tomography (CBCT) has established the gold standard for reducing treatment failures due to these morphological variations and hence, optimizing endodontic therapy clinical results.¹⁰² However, due to its increased radiation dosage compared to traditional radiography, CBCT is not widely employed.¹⁰³ To address such issues, AI has been applied to categorize the supplied data using a CNN to determine the anatomical variation in root canals.¹⁰⁴^,¹⁰⁵

Oral and Maxillofacial Surgery

AI can be used in the diagnosis, surgical, and adjunctive treatment of disorders, diseases, injuries, and defects involving the functional and aesthetic aspects of the hard and soft tissues of the oral and maxillofacial regions and related structures.

AI technology has been widely applied for predicting facial attractiveness after orthognathic surgery. The above artificial intelligence models are built on either ANNs or CNNs.¹⁰⁶

Deep-learning-based algorithms can predict the virtual soft tissue profile after mandibular advancement surgery and compare its accuracy with the mass tensor model (MTM).¹⁰⁷

Orthodontics and Dentofacial Orthopedics

ANNs have enormous potential to help in clinical decision-making. To get predictable outcomes for patients, orthodontic treatments must be meticulously planned. Teeth extractions, on the other hand, are not uncommon in orthodontic treatment plans. As a result, ensuring that the best clinical choice is taken before embarking on irreversible operations is critical.¹⁰⁸^–¹¹⁰ Using DL for automated tooth segmentation on a 3D model of the jaw, the segmented teeth can be reconstructed with their roots from the CBCT using iterative closest point (ICP) algorithm to form a complete digital dental model, and to obtain the necessary information for orthodontic treatment simulation.¹¹¹^,¹¹²

Periodontics

There are two clinical categories of periodontitis, according to the American Academy of Periodontology's classification of periodontal disease in 1999: aggressive (AgP) and chronic (CP). Due to the disease's complex pathophysiology, no clinical, microbiological, histological, or genetic test can distinguish AgP patients from CP patients.¹¹³ Many non-surgical and surgical techniques have been developed to manage periodontally compromised teeth and supporting structures. Despite advancements in treatment methods, there has been no major improvement in diagnosing and forecasting the prognosis of periodontally compromised teeth (PCT).¹¹⁴ Clinical diagnosis and prognosis are highly reliant on empirical data. Deep CNN algorithms have shown a high potential for usefulness and accuracy in diagnosing and predicting the need for extraction of PCT.¹¹⁵

Oral and Maxillofacial Radiology

In detecting and identifying anatomical structures, CNNs have demonstrated promise. For instance, some people have been taught to identify and name teeth using periapical radiographs. When it comes to identifying and classifying teeth, CNN's accuracy rating of 95.8-99.45 percent closely resembles that of clinical specialists, whose accuracy score is 99.98 percent. Dental cavities can be found and diagnosed with CNNs as well. In 3000 periapical radiographs of posterior teeth, a deep CNN algorithm identified carious lesions with an accuracy of 75.5-93.3 percent and a sensitivity of 74.5-97.1 percent.¹¹⁶ With sensitivity ranging from 19 percent to 94 percent; this represents a considerable advancement over the use of radiographs alone for diagnosis by medical personnel. When combined with their speed, deep CNNs have a high potential for improving the sensitivity of dental caries diagnosis, making them one of the most effective technologies in this field.¹¹⁷^,¹¹⁸ Another application of the AI in dental radiology is the use of artefact reduction algorithms that help the enhancement of the radiographic images and eliminate the distortion effect radio-opaque objects on the fine details of the acquired image, while reducing the need for high radiation dose and large voxel size.¹¹⁹^,¹²⁰

Oral Medicine and Pathology

Early identification and diagnosis of oral lesions are critical in dental offices since early detection improves prognosis greatly. Because certain oral lesions might be precancerous or cancerous, obtaining an accurate diagnosis and treating the patient appropriately.¹²¹ CNN has been demonstrated to be a potential tool in diagnosing head and neck cancer lesions. CNN offers excellent promise for recognizing tumoral tissues in tissue samples or on radiographs, with specificity and accuracy of 78–81.8 percent and 80–83.3 percent, respectively (compared to specialists' 83.2 percent and 82.9 percent, respectively).¹¹⁷

Pediatric Dentistry

AI in Pediatric Dentistry can be used in the same way it is used in adults by providing primary and complete preventative and therapeutic oral health diagnosis, care, and consultation knowledge for infants and kids through adulthood.

AI has the potential to solve discrepancies that may arise throughout the analysis of growth data, and AR augmented reality methodologies have been developed to educate patients and families about growth disorders and their treatments.¹²²

Prosthodontics

AI models serve as a dependable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures.¹⁰⁵^,¹¹⁴ (See Table 1) which summarizes a set of applications for dental activities that integrates AI technology.

Table 1. Summary of relevant studies on artificial intelligence applications in dentistry.

The Utilization of Artificial Intelligence in Dentistry
Field of Research	AI Technique	Applications	Reference
Image segmentation	Convolutional neural networks (CNN)	Panoramic radiographs to test a novel method for automatic teeth segmentation	¹⁴³^,¹⁴⁴
Radiology	CNN	Identification and classification of dental implant systems	¹⁴⁵^,¹⁴⁶
Prosthodontics	Intrinsic AI	Tracing the margin line of the implant abutment	¹⁴⁷^,¹⁴⁸
CT scans	CNN	To develop an automated mandible segmentation technique.	¹⁴⁹^,¹⁵⁰
Panoramic radiographs	CNN	To evaluate the performance of a CNN for detecting osteoporosis	¹⁵¹^,¹⁵²
Orthodontics	Artificial neural networks (ANN)	Diagnosis of the need for orthodontic extraction	¹⁰⁸^,¹⁵³
Periodontics	CNN	Diagnosis and prediction of periodontally compromised teeth	¹⁵⁴^,¹⁵⁵
Oral medicine	ANN	To predict the occurrences of Bisphosphonate Related Osteonecrosis of the Jaw (BRONJ) associated with a dental extraction.	¹⁵⁶^,¹⁵⁷
Dental periapical radiographs	CNN	To recognize and classify teeth position	¹⁵⁸^,¹⁵⁹
Image segmentation	CNN and RNN	To develop a fully automated image analysis for mandible and anatomical landmark segmentation.	¹⁶⁰^,¹⁶¹
Dental Public Health	Fast R-CNN	Automatic teeth recognition, Craniomaxillofacial Landmark Detection	¹⁶²^,¹⁶³
Endodontics	CNN	Score periapical lesion on an intraoral periapical radiograph For diagnosis and planning of treatment in endodontics	¹⁶⁴^,⁷³
Oral and Maxillofacial Surgery	ANN, CNN, DL, MTM	Detection of ameloblastomas and keratocystic odontogenic tumors Diagnosing, treatment planning, and predicting the prognosis of orthognathic surgery	¹⁶⁵^,¹⁶⁶
Oral Medicine and Pathology	ANN and CNN	Early oral cancer diagnosis, Dental image diagnosis	¹⁶⁷^,¹⁶⁸^,¹⁴⁴
Oral and Maxillofacial Radiology	Deep CNNs	Dental and maxillofacial image analysis Detection and diagnosis of dental caries using a deep learning-based	¹⁶⁹^,¹⁷⁰
Orthodontics and Dentofacial Orthopedics	CNN	Fully automatic segmentation of sinonasal cavity and pharyngeal airway based Accuracy detection of a posteroanterior cephalometric landmark	¹⁷¹^,¹⁷²
Pediatric Dentistry	Augmented Reality (AR)	To motivate oral hygiene practice in children: protocol for the development. Evaluation of holohuman application as a novel educational tool in dentistry	¹⁷³^,¹⁷⁴
Periodontics	Deep CNN	Identify and classify dental implant systems using panoramic and periapical radiographs. Diagnosis and prediction of compromised teeth	¹⁷⁵^,¹⁵⁴
Prosthodontics	ML	Oral and craniofacial imaging Tooth-supported fixed and removable prosthodontics	¹⁷⁶^,¹¹⁴
Application of Artificial Intelligence in 3D Digital Dentistry
CAD/CAM	CNN	Estimate the debonding probability of CAD/CAM crowns	¹⁷⁷
	Intrinsic AI and algorithms of CAD software	Automatically trace the margin line of the implant abutment through subgingival.	¹⁷⁸
	ML models (RF, ET, LightBM, CBDT, and XGBoost)	Predict the flexural strength of CAD/CAM resin composite blocks (RCBs)	¹⁷⁹
	CNN	CAD/CAM implant dentistry planning using three-dimensional cone-beam computed tomography (CBCT) images	¹⁸⁰
3D printing	(ANN) supported by genetic algorithms (GA)	Optimization of the 3D-printing process in terms of features and material selection	¹⁸¹
	DL	Fabrication and maturation of 3D bioprinter tissues and organs	¹⁸²
	DL-based PointNet++ model	3D Printing of Tooth Model	¹⁸³
3D scanning	CNN	Enhancing the resolution of CT image assessment	¹⁸⁴
	ML	Diagnosis and planning in plastic and reconstructive surgery	¹⁸⁵
	CNN	Tooth segmentation	¹⁸⁶
	Generative adversarial network	Tooth segmentation	¹⁸⁷
	CNN	Automated tooth labeling	¹⁸⁸

AI Applications in 3D Digital Dentistry

Digital dentistry has changed the dental industry by introducing fresh methods, tools, and interactions. Research directions and opportunities for material scientists have been prompted by innovation. Many digital processes for production processing, particularly computer-aided designing and manufacturing (CAD/CAM), have already been integrated into dental treatment procedures.¹²³^–¹²⁶

Nowadays, the advances in Artificial Intelligence (AI) and 3D imaging systems make dentists rely more and more on digital technologies for detection and treatment.¹²⁷^,¹²⁸ Adopting AI-based digital technology for clinical solutions and dental laboratories allows the dentist to digitally repeat the whole procedure and treatments. Furthermore, assure sharing of information across the entire process of the digital dental system, shown in Figure 7. Using a digitalized dental system brings real advantages in accuracy and quality, is time-consuming, and decreases costs.¹²⁹

Figure 7. The process of digitalizing the dental system.

Digital dentistry now incorporates diagnostics, decision-making, treatment conduct, and re-evaluation of lifelong management of patient's oral health, as well as computer-aided designing and manufacturing (CAD/CAM) and rapid prototyping fields.

Rapid prototyping (RP) is a subset of rapid manufacturing and a type of (CAM). It is a method of creating three-dimensional (3D) models of a finished product or a component of a larger whole using 3D printers quickly and automatically. The recent revolutionary application of 3D scanning and printing is AI-based orthodontic treatment.¹³⁰^–¹³² AI is applied in orthodontics at all stages, from diagnosis to treatment planning and follow-up supervision. Where the Aligners and treatments can be designed with the help of these 3D scans. After the aligners are printed, a data algorithm is developed that intelligently specifies how the teeth should be managed to move, the amount of pressure that should be applied, and sometimes even recognizes the pressure points for that particular tooth.¹³³

Large dental labs in Europe and USA have increasingly implemented CAD/CAM technology. Recent advancements in 3D printing have broadened its applications that involve dental models, orthodontic appliances, mouth guards, and appliances for guided implant surgery.

All CAD/CAM systems are made up of three parts:

• 3D Scanners convert geometric features into digital data that a computer can process.
• CAD is a data processing software that produces a dataset for the component to be manufactured, depending on the application.
• CAM is a manufacturing technology that transforms a dataset into the desired component.

CAD/CAM technology has revolutionized the concept of dentistry. Standardized production processes have significantly improved the quality of dental prostheses. This provides extremely effective quality management. It enhanced productivity significantly and changed dental laboratories from manufacturers into sophisticated computerized production centers. Increased productivity. In addition, it leads to a competitive capability to make dental prostheses independent of the manufacturing site, which could be a crucial reason for high-wage countries to maintain business volume in the country.¹³⁴ Lastly, CAD/CAM technology has enabled the high-precision machining of novel materials such as high-performance ceramics and titanium.

Recently, researchers have been looking into ways to improve the accuracy of 3D scanning, CAD\CAM, and 3D printing, such as using AI models. This can save money and time and reduce the number of visits required for precise fitting. Table 1 includes a summary of the applications of AI in 3D digital dentistry during recent years.

Even so, some drawbacks of this manufacturing technology should be handled. The rising cost of machines may exceed the limits of smaller laboratories. Additionally, some applications are limited due to software and manufacturing procedures. CAD/CAM technology has already changed dentistry and will gradually replace traditional methods of fabricating dental restorations.

Strengths and Weaknesses of AI in Dentistry

Strengths of AI in Dentistry

AI enables the integration of many heterogeneous data domains, such as patient data, clinical knowledge of the patient, imaging results, and so on, creating the greatest use of these data obtained and understanding their contact. Artificial intelligence-based supports discoveries by supplementing other research levels and current modeling methodologies within silico experimentation choices in traditional research hierarchies.

As previously noted, AI may simplify mundane tasks and enhance the contact time between patient and doctor. This might include not just diagnostic aid technology but also data from patients' voices and speech to help the dentist keep track of the record and help in saving time.¹³⁵

Using these continually obtained data will reduce the effect of "on-off-medicine," in which patients only meet the doctor for a few seconds. However, the health issues develop over the year leading to an increase and decrease in the symptoms of the disease over time. Continuous and constant health and behavior analysis will allow for a far deeper, personalized knowledge of the underlying drivers and processes of health and illness.¹³⁶

Weaknesses of AI in Dentistry

Diagnostic and treatment expenses are expected to reduce due to betterment in the healthcare field by embedding AI in the system resulting in more early diagnoses of chronic diseases. However, the disadvantage of the use of AI in dentistry includes.¹³⁷

AI may also help alleviate labor scarcities, which have been noted and are projected to remain in many regions of the world, therefore assisting in achieving the sustainable development goals defined by WHO.

AI-based apps will expedite treatment, freeing the dental staff of time-consuming, mundane duties, improving health at a cheaper cost for a larger population, and eventually enabling customized, predictive, preventative, and participative dentistry. However, AI solutions have not yet been widely adopted in routine dental practice, owing to fewer data and development standards and ethical concerns.³⁴

The misuse of data and security concerns of AI is other important parameter in dentistry. relying entirely on the machine to decide the health care services is critical, and entrusting a machine will not be okay regarding human health. It would be suitable only if AI provides low cost with excellent patient benefits and, as a result, improve society.¹³⁸ Figure 8 provides of summary of the strengths and weaknesses of AI within the dentistry area.

Figure 8. Strengths and weaknesses of AI.

The Main Challenges of using AI in Dentistry

Clinical data storage and interchange present significant challenges when using AI algorithms in the healthcare industry. The initial training of the AI system and further training, validation, and improvement require patient-specific data. Additionally, the development of AI will promote data flow within different businesses and, in some circumstances, across international boundaries. When integrating AI into healthcare operations, systems must be changed to protect patient privacy and confidentiality. Personal data must therefore be anonymized before further distribution is considered. Despite being able to implement these precautions, the healthcare industry is still dubious about secure data sharing.¹³⁹

AI systems raise safety issues as well. It is necessary to create controls for the effectiveness of AI algorithms. The US Food and Medicine Administration created a new drug classification called "Software as Medical Device" to address this problem,¹⁴⁰ which is in charge of ensuring patient safety and safe innovation. Ambiguous responsibility in applying AI technologies is a further cause for concern. The replacement of people with autonomous agents raises a number of legal and ethical concerns because the legal system is based on the core tenet that blame and crime are ultimately attributed to people. For the foreseeable future, these challenges will continue to pose a significant challenge to the judicial system.¹⁴¹

The openness of AI algorithms and data is a major concern. The precision of the annotations and labeling of the training dataset significantly influences the quality of predictions generated by AI systems. Poor outcomes may stem from wrongly tagged data. Clinic-labeled datasets could be of different quality, making the developed AI systems less effective. Additionally, healthcare professionals should be able to argue the results and projections of an AI system and have a complete understanding of them. The interpretability of AI technology is a well-known problem, and much advancement is required before some algorithms, like neural networks, can provide clinical diagnoses or treatment recommendations with total transparency.¹⁴²

Conclusion

In the recent decade, the field of AI has evolved enormously. While breakthroughs in AI, such as neural networking, natural language processing, image identification, and speech recognition, have revolutionized medicine and dentistry in many ways, they are not without downsides and obstacles. One example is the high upfront capital equipment expenses. When applied to clinical medicine, the methods sought for adoption in the medical profession have certain severe limitations. At some point, simple flowcharts and statistical pattern matching become unmanageable. Algorithms frequently make incorrect assumptions, leading to doubt regarding their responsibility in healthcare. Although AI systems are a big help in the field of dentistry and dental education, biological processes are far more complicated, and AI systems will never be able to replace human knowledge, competence, and decision-making capacity.

Despite numerous studies showing potential applications of AI in dentistry, these systems are far from being able to completely replace dental professionals. AI should be viewed as an additional benefit for dentists and other professionals. To ensure that humans can continue to oversee the care and make informed judgments in dentistry, AI must be implemented in a safe and controlled manner. Most institutions are currently unprepared for the duty of providing dental and continuing education training, which is necessary for effective AI integration in dentistry. Additionally, augmented reality (AR) and virtual reality (VR) both benefit from AI. Mixed reality is a novel concept that incorporates parts of generative AI, VR, and AR into computer-superimposed information overlays to improve learning and surgical planning. As many AI systems for various dentistry disciplines are being researched and have yielded promising early results, a future for AI in the healthcare system cannot be ruled out. AI systems offer significant promise as a valuable tool for oral health practitioners.

The results of this review indicate that embracing AI-based technologies for clinical solutions and dental laboratories enables dentists to digitally repeat procedures and treatments and ensure data sharing throughout the entire digital dental system process. Furthermore, it provides significant benefits in accuracy and quality, as well as saving time and money.

References

1. Kong S-C, Cheung WM-Y, Zhang G: Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports. 2022; 7: 100223. Publisher Full Text
2. Obaideen K, Olabi AG, Al Swailmeen Y, et al.: Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs). Sustainability. 2023; 15(2): 1418. Publisher Full Text
3. Stranieri A, Venkatraman S, Minicz J, et al.: Emerging point of care devices and artificial intelligence: Prospects and challenges for public health. Smart Health. 2022/06/01 2022; 24: 100279. Publisher Full Text
4. Mamun Q: Blockchain technology in the future of healthcare. Smart Health. 2022/03/012022; 23: 100223. Publisher Full Text
5. Pourhomayoun M, Shakibi M: Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021/04/012021; 20: 100178. Publisher Full Text
6. Rahman MJ, Morshed BI, Harmon B, et al.: A pilot study towards a smart-health framework to collect and analyze biomarkers with low-cost and flexible wearables. Smart Health. 2022/03/012022; 23: 100249. Publisher Full Text
7. Abdelkareem MA, Soudan B, Mahmoud MS, et al.: Progress of artificial neural networks applications in hydrogen production. Chem. Eng. Res. Des. 2022; 182: 66–86. Publisher Full Text
8. Salameh T, Kumar PP, Olabi A, et al.: Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals. Journal of Energy Storage. 2022; 55: 105312. Publisher Full Text
9. Guo K, Yang Z, Yu C-H, et al.: Artificial intelligence and machine learning in design of mechanical materials. Materials Horizons. 2021; 8(4): 1153–1172. Publisher Full Text
10. Hashimoto DA, Rosman G, Rus D, et al.: Artificial intelligence in surgery: promises and perils. Ann. Surg. 2018; 268(1): 70–76. Publisher Full Text
11. Kaziha O, Bonny T: A comparison of quantized convolutional and lstm recurrent neural network models using mnist. IEEE. 2019; 1–5.
12. Fiske A, Henningsen P, Buyx A: Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J. Med. Internet Res. 2019; 21(5): e13216. PubMed Abstract | Publisher Full Text | Free Full Text
13. Siau K, Wang W: Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM). 2020; 31(2): 74–87. Publisher Full Text
14. Vrontis D, Christofi M, Pereira V, et al.: Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Int. J. Hum. Resour. Manag. 2022; 33(6): 1237–1266. Publisher Full Text
15. Patil R, Kumar S: Bibliometric survey on diagnosis of plant leaf diseases using artificial intelligence. International Journal of Modern Agriculture. 2020; 9(3): 1111–1131.
16. van Hoek J , Huber A, Leichtle A, et al.: A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur. J. Radiol. 2019; 121: 108742. PubMed Abstract | Publisher Full Text
17. Al Nassan W, Bonny T, Obaideen K: A Customized Convolutional Neural Network for Dental Bitewing Images Segmentation. presented at: The International Conference on Electrical and Computing Technologies and Applications, 2022 (ICECTA’2022) 2022.
18. Al Nassan W, Bonny T, Obaideen K, et al.: AN LSTM model-based Prediction of Chaotic System: Analyzing the Impact of Training Dataset Precision on the Performance. IEEE.2022; 337–342.
19. Shehata N, Obaideen K, Sayed ET, et al.: Role of Refuse-derived fuel in circular economy and sustainable development goals. Process Saf. Environ. Prot. 2022; 163: 558–573. Publisher Full Text
20. Vinuesa R, Azizpour H, Leite I, et al.: The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020; 11(1): 1–10.
21. Fernandez RM: SDG3 good health and well-being: integration and connection with other SDGs. Good Health and Well-Being. 2020; 629–636. Publisher Full Text
22. Murphy K, Di Ruggiero E, Upshur R, et al.: Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med. Ethics. 2021; 22(1): 1–17.
23. Shan T, Tay F, Gu L: Application of artificial intelligence in dentistry. J. Dent. Res. 2021; 100(3): 232–244. Publisher Full Text
24. Fadzly N, Zuharah WF, Jenn Ney JW: Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees. Plant Signal. Behav. 2021; 16(10): 1935605. PubMed Abstract | Publisher Full Text | Free Full Text
25. Hung KF, Ai QYH, King AD, et al.: Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network. Clin. Oral Investig. 2022; 26(5): 3987–3998. PubMed Abstract | Publisher Full Text
26. Achararit P, Manaspon C, Jongwannasiri C, et al.: Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks. European Journal of Dentistry. 2023. Publisher Full Text
27. Chen Y-w, Stanley K, Att W: Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020; 51(3): 248–257.
28. Reddy MS, Shetty SR, Vannala V: Embracing personalized medicine in dentistry. J. Pharm. Bioallied Sci. 2019; 11(Suppl 2): S92–S96. PubMed Abstract | Publisher Full Text
29. Nijman S, Leeuwenberg A, Beekers I, et al.: Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review. J. Clin. Epidemiol. 2022; 142: 218–229. Publisher Full Text
30. Lahoud P, Jacobs R, Boisse P, et al.: Precision medicine using patient-specific modelling: State of the art and perspectives in dental practice. Clin. Oral Investig. 2022; 26(8): 5117–5128. PubMed Abstract | Publisher Full Text
31. Fatima A, Shafi I, Afzal H, et al.: Advancements in dentistry with artificial intelligence: current clinical applications and future perspectives. MDPI. 2022; 10: 2188. Publisher Full Text
32. Khanagar SB, Al-Ehaideb A, Maganur PC, et al.: Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of dental sciences. 2021; 16(1): 508–522. PubMed Abstract | Publisher Full Text | Free Full Text
33. Tandon D, Rajawat J, Banerjee M: Present and future of artificial intelligence in dentistry. Journal of oral biology and craniofacial research. 2020; 10(4): 391–396. Publisher Full Text
34. Schwendicke F, Samek W, Krois J: Artificial intelligence in dentistry: chances and challenges. J. Dent. Res. 2020; 99(7): 769–774. PubMed Abstract | Publisher Full Text | Free Full Text
35. Bindushree V, Sameen R, Vasudevan V, et al.: Artificial intelligence: In modern dentistry. Journal of Dental Research and Review. 2020; 7(1): 27.
36. Bi Q, Goodman KE, Kaminsky J, et al.: What is machine learning? A primer for the epidemiologist. Am. J. Epidemiol. 2019; 188(12): 2222–2239.
37. Khamparia A, Singh KM: A systematic review on deep learning architectures and applications. Expert. Syst. 2019; 36(3): e12400. Publisher Full Text
38. Grollmisch S, Cano E, Kehling C, et al.: Analyzing the potential of pre-trained embeddings for audio classification tasks. IEEE. 2021; 790–794.
39. do Nascimento Gerhardt M , Fontenele RC, Leite AF, et al.: Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks. J. Dent. 2022; 122: 104139. PubMed Abstract | Publisher Full Text
40. Mahmoud S, Irwin B, Chekmarev D, et al.: Imputation of sensory properties using deep learning. J. Comput. Aided Mol. Des. 2021; 35(11): 1125–1140. Publisher Full Text
41. Tian H, Chen S-C, Shyu M-L: Evolutionary programming based deep learning feature selection and network construction for visual data classification. Inf. Syst. Front. 2020; 22(5): 1053–1066. Publisher Full Text
42. Zhou X-Y, Guo Y, Shen M, et al.: Application of artificial intelligence in surgery. Front. Med. 2020; 14(4): 417–430. PubMed Abstract | Publisher Full Text
43. Shen J, Zhang CJ, Jiang B, et al.: Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med. Inform. 2019; 7(3): e10010. PubMed Abstract | Publisher Full Text | Free Full Text
44. Yamada M, Saito Y, Imaoka H, et al.: Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci. Rep. 2019; 9(1): 1–9.
45. Mishra A, Yadav P, Kim S: Artificial Intelligence Accelerators. Artificial Intelligence and Hardware Accelerators. Springer; 2023; 1–52.
46. Bonny T, Henkel J: Instruction splitting for efficient code compression.2007; 646–651.
47. Bonny T, Haq A: Emulation of high-performance correlation-based quantum clustering algorithm for two-dimensional data on FPGA. Quantum Inf. Process. 2020; 19: 1–21.
48. Bonny T, Henkel J: Using Lin-Kernighan Algorithm for Look-up Table compression to improve code density.2006; 259–265.
49. Sambas A, Vaidyanathan S, Zhang X, et al.: A Novel 3D Chaotic System with Line Equilibrium: Multistability, Integral Sliding Mode Control, Electronic Circuit, FPGA and its Image Encryption. IEEE Access. 2022; 10: 68057–68074. Publisher Full Text
50. Bonny T: Chaotic or Hyper-chaotic Oscillator? Numerical Solution, Circuit Design, MATLAB HDL-Coder Implementation, VHDL Code, Security Analysis, and FPGA Realization. Circuits, Systems, and Signal Processing. 2021/03/01 2021; 40(3): 1061–1088. Publisher Full Text
51. Bonny T, Kashkash M, Ahmed F: An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination. Desalination. 2022/01/152022; 522: 115443. Publisher Full Text
52. Punith Kumar CR, Natarajan R, Padma K, et al.: ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A BRIEF REVIEW. Suranaree Journal of Science & Technology. 2022; 29(2). Publisher Full Text
53. Grischke J, Johannsmeier L, Eich L, et al.: Dentronics: Towards robotics and artificial intelligence in dentistry. Dent. Mater. 2020/06/01/2020; 36(6): 765–778. PubMed Abstract | Publisher Full Text
54. Jiang F, Jiang Y, Zhi H, et al.: Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017; 2(4): 230–243. PubMed Abstract | Publisher Full Text | Free Full Text
55. Wen X, Leng P, Wang J, et al.: Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC bioinformatics. 2022; 23(1): 1–20. Publisher Full Text
56. Thrall JH, Li X, Li Q, et al.: Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J. Am. Coll. Radiol. 2018; 15(3): 504–508. PubMed Abstract | Publisher Full Text
57. Kaziha O, Jarndal A, Bonny T: Genetic Algorithm Augmented Convolutional Neural Network for Image Recognition Applications. IEEE. 2020; 1–5.
58. Nasir N, Kansal A, Barneih F, et al.: Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. Intelligent Systems with Applications. 2023; 17: 200160. Publisher Full Text
59. Barneih F, Nasir N, Alshaltone O, et al.: Artificial neural network model using short-term fourier transform for epilepsy seizure detection. IEEE. 2022; 1–5.
60. Orrù G, Pettersson-Yeo W, Marquand AF, et al.: Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. Apr 2012; 36(4): 1140–1152. PubMed Abstract | Publisher Full Text
61. Nasir N, Oswald P, Barneih F, et al.: Hypertension classification using machine learning part II. IEEE. 2021; 459–463.
62. Fiandaca MS, Mapstone M, Mahmoodi A, et al.: Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls. PLoS One. 2018; 13(4): e0195318. PubMed Abstract | Publisher Full Text | Free Full Text
63. Manne R, Kantheti SC: Application of artificial intelligence in healthcare: chances and challenges. Current Journal of Applied Science and Technology. 2021; 40(6): 78–89. Publisher Full Text
64. Chiappelli F, Balenton N, Khakshooy A: Future innovations in viral immune surveillance: a novel place for bioinformation and artificial intelligence in the administration of health care. Bioinformation. 2018; 14(5): 201–205. PubMed Abstract | Publisher Full Text | Free Full Text
65. Surovková J, Haluzová S, Strunga M, et al.: The New Role of the Dental Assistant and Nurse in the Age of Advanced Artificial Intelligence in Telehealth Orthodontic Care with Dental Monitoring: Preliminary Report. Appl. Sci. 2023; 13(8): 5212. Publisher Full Text
66. Lahoud P, Diels S, Niclaes L, et al.: Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT. J. Dent. 2022/01/01 2022; 116: 103891. Publisher Full Text
67. Engels P, Meyer O, Schönewolf J, et al.: Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs. J. Dent. 2022/06/01 2022; 121: 104124. PubMed Abstract | Publisher Full Text
68. Fontenele RC, Gerhardt MN, Pinto JC, et al.: Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images – A validation study. J. Dent. 2022/04/01 2022; 119: 104069. PubMed Abstract | Publisher Full Text
69. Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al.: Deep learning for caries detection: A systematic review. J. Dent. 2022/07/01 2022; 122: 104115. Publisher Full Text
70. Haidar ZS: Digital Dentistry: Past, Present, and Future. Digital Medicine and Healthcare Technology. 2023; 2. Publisher Full Text
71. Karantanas AH, Efremidis S: The concept of the invisible radiologist in the era of artificial intelligence. Eur. J. Radiol. 2022; 155: 110147. PubMed Abstract | Publisher Full Text
72. Kim T, Cha M, Kim H, et al.: Learning to discover cross-domain relations with generative adversarial networks. PMLR. 2017; 1857–1865.
73. Asiri AF, Altuwalah AS: The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review. The Saudi Dental Journal. 2022; 34: 270–281. PubMed Abstract | Publisher Full Text | Free Full Text
74. Naylor CD: On the prospects for a (deep) learning health care system. JAMA. 2018; 320(11): 1099–1100. PubMed Abstract | Publisher Full Text
75. Rokhshad R, Keyhan SO, Yousefi P: Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review. Maxillofacial Plastic and Reconstructive Surgery. 2023; 45(1): 14. PubMed Abstract | Publisher Full Text | Free Full Text
76. Nielsen MA: Neural networks and deep learning. San Francisco, CA, USA: Determination press; 2015; vol. 25. .
77. Cao C, Cerfolio RJ: Virtual or augmented reality to enhance surgical education and surgical planning. Thorac. Surg. Clin. 2019; 29(3): 329–337. Publisher Full Text
78. Durham M, Engel B, Ferrill T, et al.: Digitally augmented learning in implant dentistry. Oral and Maxillofacial Surgery Clinics. 2019; 31(3): 387–398. PubMed Abstract | Publisher Full Text
79. Kikuchi H, Ikeda M, Araki K: Evaluation of a virtual reality simulation system for porcelain fused to metal crown preparation at Tokyo Medical and Dental University. J. Dent. Educ. 2013; 77(6): 782–792. Publisher Full Text
80. Khanna SS, Dhaimade PA: Artificial intelligence: transforming dentistry today. Indian J. Basic. Appl. Med. Res. 2017; 6(3): 161–167.
81. Wiederhold MD, Gao K, Wiederhold BK: Clinical use of virtual reality distraction system to reduce anxiety and pain in dental procedures. Cyberpsychol. Behav. Soc. Netw. 2014; 17(6): 359–365. PubMed Abstract | Publisher Full Text | Free Full Text
82. Joda T, Gallucci G, Wismeijer D, et al.: Augmented and virtual reality in dental medicine: A systematic review. Comput. Biol. Med. 2019; 108: 93–100. Publisher Full Text
83. Dutã M, Amariei CI, Bogdan CM, et al.: An overview of virtual and augmented reality in dental education. Oral. Health Dent. Manag. 2011; 10: 42–49.
84. Kazi H, Haddawy P, Suebnukarn S: Leveraging a domain ontology to increase the quality of feedback in an intelligent tutoring system. Springer; 2010; 75–84.
85. Park WJ, Park J-B: History and application of artificial neural networks in dentistry. European journal of dentistry. 2018; 12(04): 594–601. Publisher Full Text
86. Tamaki Y, Nomura Y, Katsumura S, et al.: Construction of a dental caries prediction model by data mining. J. Oral Sci. 2009; 51(1): 61–68. PubMed Abstract | Publisher Full Text
87. Kumagai A, Jeong S, Kim D, et al.: Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults. Sci. Rep. 2023; 13(1): 726. PubMed Abstract | Publisher Full Text | Free Full Text
88. Association CD: Dental Specialties. CDSA ACSD;
89. Rekow ED: Digital dentistry: The new state of the art—Is it disruptive or destructive? Dent. Mater. 2020; 36(1): 9–24. Publisher Full Text
90. Sharma H, Suprabha BS, Rao A: Teledentistry and its applications in paediatric dentistry: A literature review. Pediatr. Dent. J. 2021; 31(3): 203–215. PubMed Abstract | Publisher Full Text | Free Full Text
91. Gleeson H, Kalsi A: Remote clinical consultations in restorative dentistry—A survey. J. Dent. 2022; 117: 103914. Publisher Full Text
92. Kannan P: Artificial Intelligence. Applications in healthcare, Asian hospital and healthcare management. 2019; 19.
93. Esmaeili MA: New chapter in healthcare environmental impact reduction through medical treatment choices: The case of diagnostic imaging services. Wichita State University; 2016.
94. Tong W, Nugent S, Jensen G, et al.: An algorithm for locating landmarks on dental X-rays. IEEE. 1989; 552–554.
95. Yeager D: What Will Happen When Artificial Intelligence Comes to Radiology? Radiology Today. 2016; 17(5): 12.
96. Xie X, Wang L, Wang A: Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010; 80(2): 262–266. PubMed Abstract | Publisher Full Text | Free Full Text
97. Kattadiyil MT, Mursic Z, AlRumaih H, et al.: Intraoral scanning of hard and soft tissues for partial removable dental prosthesis fabrication. J. Prosthet. Dent. 2014; 112(3): 444–448. PubMed Abstract | Publisher Full Text
98. Vera V, Corchado E, Redondo R, et al.: Applying soft computing techniques to optimise a dental milling process. Neurocomputing. 2013; 109: 94–104. Publisher Full Text
99. Zakirulla M, Javed S, Assiri NEH, et al.: An overview of artificial neural network in the field of pediatric dentistry. Journal of Dental and Orofacial Research. 2020; 16(1): 20–25.
100. Seol Y-J, Kang H-W, Lee SJ, et al.: Bioprinting technology and its applications. Eur. J. Cardiothorac. Surg. 2014; 46(3): 342–348. Publisher Full Text
101. Zhu Y, Xu T, Peng L, et al.: Faster-RCNN based intelligent detection and localization of dental caries. Displays. 2022; 74: 102201. Publisher Full Text
102. Shaheen E, Leite A, Alqahtani KA, et al.: A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. J. Dent. 2021/12/012021; 115: 103865. PubMed Abstract | Publisher Full Text
103. Umer F, Habib S: Critical analysis of artificial intelligence in endodontics: a scoping review. J. Endod. 2021; 48: 152–160. Publisher Full Text
104. Hiraiwa T, Ariji Y, Fukuda M, et al.: A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 2019; 48(3): 20180218. PubMed Abstract | Publisher Full Text | Free Full Text
105. Qu Y, Lin Z, Yang Z, et al.: Machine learning models for prognosis prediction in endodontic microsurgery. J. Dent. 2022/03/012022; 118: 103947. PubMed Abstract | Publisher Full Text
106. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al.: Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. Journal of dental sciences. 2021; 16(1): 482–492. PubMed Abstract | Publisher Full Text | Free Full Text
107. Ter Horst R, van Weert H , Loonen T, et al.: Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning. J. Cranio-Maxillofac. Surg. 2021; 49(9): 775–782. PubMed Abstract | Publisher Full Text
108. Jung S-K, Kim T-W: New approach for the diagnosis of extractions with neural network machine learning. Am. J. Orthod. Dentofac. Orthop. 2016; 149(1): 127–133. Publisher Full Text
109. Gandedkar NH, Wong MT, Darendeliler MA: Role of Virtual Reality (VR), Augmented Reality (AR) and Artificial Intelligence (AI) in tertiary education and research of orthodontics: An insight. Elsevier; 2021; 69–77.
110. Mohammad-Rahimi H, Nadimi M, Rohban MH, et al.: Machine learning and orthodontics, current trends and the future opportunities: a scoping review. Am. J. Orthod. Dentofac. Orthop. 2021; 160(2): 170–192.e4. Publisher Full Text
111. Wu T-H, Lian C, Lee S, et al.: Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE Trans. Med. Imaging. 2022; 41(11): 3158–3166. PubMed Abstract | Publisher Full Text
112. Wang H, Minnema J, Batenburg KJ, et al.: Multiclass CBCT image segmentation for orthodontics with deep learning. J. Dent. Res. 2021; 100(9): 943–949. PubMed Abstract | Publisher Full Text | Free Full Text
113. Revilla-León M, Gómez-Polo M, Barmak AB, et al.: Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J. Prosthet. Dent. 2022. PubMed Abstract | Publisher Full Text
114. Revilla-León M, Gómez-Polo M, Vyas S, et al.: Artificial intelligence models for tooth-supported fixed and removable prosthodontics: a systematic review. J. Prosthet. Dent. 2021.
115. Nguyen TT, Larrivée N, Lee A, et al.: Use of artificial intelligence in dentistry: current clinical trends and research advances. J. Can. Dent. Assoc. 2021; 87(l7): 1488–2159.
116. Tadinada A: Artificial intelligence, machine learning, and the human interface in medicine: Is there a sweet spot for oral and maxillofacial radiology? Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2019/04/01/2019; 127(4): 265–266. PubMed Abstract | Publisher Full Text
117. Tuzoff DV, Tuzova LN, Bornstein MM, et al.: Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology. 2019; 48(4): 20180051. PubMed Abstract | Publisher Full Text | Free Full Text
118. Brignardello-Petersen R: Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images. J. Am. Dent. Assoc. 2020; 151(9): e83. PubMed Abstract | Publisher Full Text
119. Uysal S, Akcicek G, Yalcin ED, et al.: The influence of voxel size and artifact reduction on the detection of vertical root fracture in endodontically treated teeth. Acta Odontol. Scand. 2021; 79(5): 354–358. Publisher Full Text
120. Queiroz PM, Groppo FC, Oliveira ML, et al.: Evaluation of the efficacy of a metal artifact reduction algorithm in different cone beam computed tomography scanning parameters. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2017; 123(6): 729–734. PubMed Abstract | Publisher Full Text
121. Al-Rawi N, Sultan A, Rajai B, et al.: The Effectiveness of Artificial Intelligence in Detection of Oral Cancer. Int. Dent. J. 2022; 72: 436–447. PubMed Abstract | Publisher Full Text | Free Full Text
122. Dunkel L, Fernandez-Luque L, Loche S, et al.: Digital technologies to improve the precision of paediatric growth disorder diagnosis and management. Growth Hormon. IGF Res. 2021; 59: 101408. PubMed Abstract | Publisher Full Text
123. Al-Dwairi ZN, Al Haj Ebrahim AA, Baba NZ: A Comparison of the Surface and Mechanical Properties of 3D Printable Denture-Base Resin Material and Conventional Polymethylmethacrylate (PMMA). J. Prosthodont. 2022; 32: 40–48. Publisher Full Text
124. Al Hamad KQ, Al Quran FA, Jwaied SZ, et al.: Effect of CAD/CAM Bur Deterioration on the Surface Roughness of Ceramic Crowns. J. Prosthodont. 2022; 31(4): 320–325. PubMed Abstract | Publisher Full Text
125. Al-Dwairi ZN, Tahboub KY, Baba NZ, et al.: A Comparison of the Flexural and Impact Strengths and Flexural Modulus of CAD/CAM and Conventional Heat-Cured Polymethyl Methacrylate (PMMA). J. Prosthodont. 2020; 29(4): 341–349. PubMed Abstract | Publisher Full Text
126. Olley RC, Alhaij S, Mohsen BM, et al.: Novel Confocal-Laser-Scanning-Microscopy and conventional measures investigating eroded dentine following dentifrice dab-on and brushing abrasion. Heliyon. 2020/03/01/2020; 6(3): e03282. PubMed Abstract | Publisher Full Text | Free Full Text
127. Mangano FG, Hauschild U, Veronesi G, et al.: Trueness and precision of 5 intraoral scanners in the impressions of single and multiple implants: a comparative in vitro study. BMC Oral Health. 2019/06/06 2019; 19(1): 101. Publisher Full Text
128. Apostolakis D, Michelinakis G, Kamposiora P, et al.: The current state of computer assisted orthognathic surgery: A narrative review. J. Dent. 2022/04/01/2022; 119: 104052. PubMed Abstract | Publisher Full Text
129. Joda T, Bornstein MM, Jung RE, et al.: Recent Trends and Future Direction of Dental Research in the Digital Era. Int. J. Environ. Res. Public Health. 2020; 17(6): 1987. PubMed Abstract | Publisher Full Text | Free Full Text
130. O'Toole S, Bernabe E, Bartlett D, et al.: The influence of scanner precision and analysis software on quantifying 3D intraoral changes: Two-factor factorial experimental design. J. Med. Internet Res. 2020; 22. Publisher Full Text
131. Charalambous P, O’Toole S, Austin R, et al.: The threshold of an intra oral scanner to measure lesion depth on natural unpolished teeth. Dent. Mater. 2022; 38: 1354–1361. PubMed Abstract | Publisher Full Text | Free Full Text
132. Revilla-León M, Zeitler JM, Barmak AB, et al.: Accuracy of the 3-dimensional virtual patient representation obtained by using 4 different techniques: An in vitro study. J. Prosthet. Dent. 2022/06/27/2022. PubMed Abstract | Publisher Full Text
133. Oliveros-López L-G, Castillo-de-Oyagüe R, Serrera-Figallo M-Á, et al.: Bone Loss in Bruxist Patients Wearing Dental Implant Prostheses: A Finite Element Analysis. Metals. 2020; 10(9): 1132. Publisher Full Text
134. Al-Hassiny A: Fundamentals of Computer-Aided Design (CAD) in Dental Healthcare: From Basics to Beyond. 3D Printing in Oral Health Science: Applications and Future Directions. Springer; 2022; 93–119.
135. Israni ST, Verghese A: Humanizing artificial intelligence. JAMA. 2019; 321(1): 29–30. Publisher Full Text
136. Engelmann L: Into the Deep-AI and Total Pathology: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Topol E, editor. New York: Basic Books; 2010. 400 pp., $17.99 (paperback), ISBN 9781541644649. Taylor & Francis; 2020.
137. Schwendicke F, Mertens S, Cantu AG, et al.: Cost-effectiveness of AI for caries detection: randomized trial. J. Dent. 2022; 119: 104080. PubMed Abstract | Publisher Full Text
138. Watt RG, Daly B, Allison P, et al.: Ending the neglect of global oral health: time for radical action. Lancet. 2019; 394(10194): 261–272. PubMed Abstract | Publisher Full Text
139. He J, Baxter SL, Xu J, et al.: The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019; 25(1): 30–36. PubMed Abstract | Publisher Full Text | Free Full Text
140. Kario K, Harada N, Okura A: The first software as medical device of evidence-based hypertension digital therapeutics for clinical practice. Hypertens. Res. 2022; 45(12): 1899–1905. PubMed Abstract | Publisher Full Text | Free Full Text
141. Klonoff DC, Gutierrez A, Fleming A, et al.: Levy Diabetes Technology & Therapeutics.May 2019.
142. Redman TC: If your data is bad, your machine learning tools are useless. Harv. Bus. Rev. 2018; 2.
143. Yang J, Xie Y, Liu L, et al.: Automated dental image analysis by deep learning on small dataset. IEEE. 2018; 492–497.
144. Schwendicke F, Golla T, Dreher M, et al.: Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent. 2019; 91: 103226. PubMed Abstract | Publisher Full Text
145. Lee J-H, Jeong S-N: Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020; 99(26): e20787. Publisher Full Text
146. Pauwels R: A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral Radiol. 2021/01/01 2021; 37(1): 153–160. PubMed Abstract | Publisher Full Text
147. Lerner H, Mouhyi J, Admakin O, et al.: Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020/03/19 2020; 20(1): 80. PubMed Abstract | Publisher Full Text | Free Full Text
148. Bernauer SA, Zitzmann NU, Joda T: The use and performance of artificial intelligence in prosthodontics: a systematic review. Sensors. 2021; 21(19): 6628. PubMed Abstract | Publisher Full Text | Free Full Text
149. Hwang D, Kang SK, Kim KY, et al.: Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur. J. Nucl. Med. Mol. Imaging. 2022; 49(6): 1833–1842. Publisher Full Text
150. Qiu B, Guo J, Kraeima J, et al.: Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Phys. Med. Biol. 2019; 64(17): 175020. Publisher Full Text
151. Kwon O, Yong T-H, Kang S-R, et al.: Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofacial Radiology. 2020; 49(8): 20200185. PubMed Abstract | Publisher Full Text | Free Full Text
152. Lee J-S, Adhikari S, Liu L, et al.: Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiology. 2019; 48(1): 20170344. PubMed Abstract | Publisher Full Text | Free Full Text
153. Bichu YM, Hansa I, Bichu AY, et al.: Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog. Orthod. 2021; 22(1): 1–11.
154. Lee J-H, Kim D-h, Jeong S-N, et al.: Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science. 2018; 48(2): 114–123. PubMed Abstract | Publisher Full Text | Free Full Text
155. Thanathornwong B, Suebnukarn S: Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry. 2020; 50(2): 169–174. PubMed Abstract | Publisher Full Text | Free Full Text
156. Kim DW, Kim H, Nam W, et al.: Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018; 116: 207–214. Publisher Full Text
157. Dal Pra K, Lemos C, Okamoto R, et al.: Efficacy of the C-terminal telopeptide test in predicting the development of bisphosphonate-related osteonecrosis of the jaw: a systematic review. Int. J. Oral Maxillofac. Surg. 2017; 46(2): 151–156. PubMed Abstract | Publisher Full Text
158. Soffer S, Ben-Cohen A, Shimon O, et al.: Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019; 290(3): 590–606. Publisher Full Text
159. Zhang K, Chen H, Lyu P, et al.: A relation-based framework for effective teeth recognition on dental periapical X-rays. Comput. Med. Imaging Graph. 2022; 95: 102022. Publisher Full Text
160. Torosdagli N, Liberton DK, Verma P, et al.: Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans. Med. Imaging. 2018; 38(4): 919–931. PubMed Abstract | Publisher Full Text
161. Qaddoura R, Manaseer WA, Abushariah MA, et al.: Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer. Multimed. Tools Appl. 2020; 79(29): 22027–22045. Publisher Full Text
162. Mahdi FP, Yagi N, Kobashi S: Automatic teeth recognition in dental X-ray images using transfer learning based faster R-CNN. IEEE. 2020; 16–21.
163. Chen X, Lian C, Deng HH, et al.: Fast and Accurate Craniomaxillofacial Landmark Detection via 3D Faster R-CNN. IEEE Trans. Med. Imaging. 2021; 40(12): 3867–3878. PubMed Abstract | Publisher Full Text | Free Full Text
164. Moidu NP, Sharma S, Chawla A, et al.: Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin. Oral Investig. 2022/01/01 2022; 26(1): 651–658. PubMed Abstract | Publisher Full Text
165. Poedjiastoeti W, Suebnukarn S: Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors. hir. 07 2018; 24(3): 236–241. PubMed Abstract | Publisher Full Text | Free Full Text
166. Khanagar SB, Alfouzan K, Awawdeh M, et al.: Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. Appl. Sci. 2022; 12(11): 5581. Publisher Full Text
167. Corbella S, Srinivas S, Cabitza F: Applications of deep learning in dentistry. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2021; 132(2): 225–238. Publisher Full Text
168. Romanini J, Kanomata LM, De Figueiredo RM: ARTIFICIAL INTELLIGENCE: AID IN EARLY ORAL CANCER DIAGNOSIS AT THE DENTAL CLINIC. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2020; 130(3): e282–e283. Publisher Full Text
169. Singh NK, Raza K: Progress in deep learning-based dental and maxillofacial image analysis: A systematic review. Expert Syst. Appl. 2022; 199: 116968. Publisher Full Text
170. Lee J-H, Kim D-H, Jeong S-N, et al.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 2018; 77: 106–111. PubMed Abstract | Publisher Full Text
171. Leonardi R, Giudice AL, Farronato M, et al.: Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks. Am. J. Orthod. Dentofac. Orthop. 2021; 159(6): 824–835.e1. PubMed Abstract | Publisher Full Text
172. Gil S-M, Kim I, Cho J-H, et al.: Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am. J. Orthod. Dentofac. Orthop. 2022; 161(4): e361–e371. PubMed Abstract | Publisher Full Text
173. Amantini SNSR, Montilha AAP, Antonelli BC, et al.: Using augmented reality to motivate oral hygiene practice in children: protocol for the development of a serious game. JMIR research protocols. 2020; 9(1): e10987. PubMed Abstract | Publisher Full Text | Free Full Text
174. Zafar S, Zachar JJ: Evaluation of HoloHuman augmented reality application as a novel educational tool in dentistry. Eur. J. Dent. Educ. 2020; 24(2): 259–265. PubMed Abstract | Publisher Full Text
175. Lee J-H, Jeong S-N: Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020; 99(26): e20787–e20787. PubMed Abstract | Publisher Full Text | Free Full Text
176. Ren R, Luo H, Su C, et al.: Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ. 2021; 9: e11451. PubMed Abstract | Publisher Full Text | Free Full Text
177. Yamaguchi S, Lee C, Karaer O, et al.: Predicting the debonding of CAD/CAM composite resin crowns with AI. J. Dent. Res. 2019; 98(11): 1234–1238. PubMed Abstract | Publisher Full Text
178. Lerner H, Mouhyi J, Admakin O, et al.: Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020; 20(1): 1–16. Publisher Full Text
179. Li H, Sakai T, Tanaka A, et al.: Interpretable AI Explores Effective Components of CAD/CAM Resin Composites. J. Dent. Res. 2022; 101: 1363–1371. Publisher Full Text
180. Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al.: A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med. Imaging. 2021; 21(1): 1–9.
181. Rojek I, Mikołajewski D, Dostatni E, et al.: AI-optimized technological aspects of the material used in 3D printing processes for selected medical applications. Materials. 2020; 13(23): 5437. PubMed Abstract | Publisher Full Text | Free Full Text
182. Ng WL, Chan A, Ong YS, et al.: Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual and Physical Prototyping. 2020; 15(3): 340–358. Publisher Full Text
183. Liu Y, Shang X, Shen Z, et al.: 3D Deep Learning for 3D Printing of Tooth Model. IEEE. 2019; 274–279.
184. Ariji Y, Fukuda M, Kise Y, et al.: Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2019; 127(5): 458–463. PubMed Abstract | Publisher Full Text
185. Knoops PG, Papaioannou A, Borghi A, et al.: A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci. Rep. 2019; 9(1): 1–12.
186. Zanjani FG, Moin DA, Verheij B, et al.: Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. PMLR. 2019; 557–571. Publisher Full Text
187. Kim T, Cho Y, Kim D, et al.: Tooth segmentation of 3D scan data using generative adversarial networks. Appl. Sci. 2020; 10(2): 490. Publisher Full Text
188. Lian C, Wang L, Wu T-H, et al.: Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners. IEEE Trans. Med. Imaging. 2020; 39(7): 2440–2450. PubMed Abstract | Publisher Full Text

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 20 Sep 2023

Author details Author details

¹ Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
² Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
³ Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
⁴ ‎College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
⁵ ‎Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates

Talal Bonny
Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Wafaa Al Nassan
Roles: Methodology, Resources, Writing – Original Draft Preparation, Writing – Review & Editing

Khaled Obaideen
Roles: Methodology, Resources, Visualization, Writing – Original Draft Preparation

Maryam Nooman Al Mallahi
Roles: Investigation, Resources, Writing – Original Draft Preparation

Yara Mohammad
Roles: Resources, Writing – Original Draft Preparation

Hatem M. El-damanhoury
Roles: Investigation, Project Administration, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 20 Sep 2023, 12:1179

https://doi.org/10.12688/f1000research.140204.1

Copyright

© 2023 Bonny T et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

	Views	Downloads
F1000Research	-	-
PubMed Central Data from PMC are received and updated monthly.	-	-

Citations

0

SEE MORE DETAILS

CITE

how to cite this article

Bonny T, Al Nassan W, Obaideen K et al. Contemporary Role and Applications of Artificial Intelligence in Dentistry [version 1; peer review: 1 approved, 2 approved with reservations]. F1000Research 2023, 12:1179 (https://doi.org/10.12688/f1000research.140204.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?

Key to Reviewer Statuses VIEW HIDE

ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 1

VERSION 1

PUBLISHED 20 Sep 2023

Views

16

Reviewer Report 07 Nov 2023

Dipmala Salunke, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India

Approved with Reservations

https://doi.org/10.5256/f1000research.153536.r212090

Summary of the article:

The article provides a detailed exploration of the role of Artificial Intelligence (AI) in dentistry, its potential applications, and its limitations. It covers a range of topics, including AI algorithms, the pros and ... Continue reading

Summary of the article:

The article provides a detailed exploration of the role of Artificial Intelligence (AI) in dentistry, its potential applications, and its limitations. It covers a range of topics, including AI algorithms, the pros and cons of using AI in dental specialties, and the need for AI to complement rather than replace dental professionals. The article also discusses the integration of AI with augmented and virtual reality technologies in dentistry.

Strengths of the Article:

Comprehensive Coverage: The article comprehensively explores the potential applications of AI in various dental specialties and discusses both the advantages and limitations of AI in the field.

Clarity of Purpose: The article clearly states its purpose, which is to assess the advancement of AI algorithms in dentistry and their performance in diagnosis, clinical decision-making, treatment, and prognosis prediction.

Integration with Emerging Technologies: The discussion of AI's integration with augmented reality and virtual reality is valuable, as it highlights innovative approaches to dental education and surgical planning.

Real-World Applications: The mention of AI's potential benefits in clinical solutions and dental laboratories demonstrates the practical advantages of AI in the dental field, such as data sharing, accuracy, and cost savings.

Areas for Improvement:

Ethical Considerations: While the article touches on the limitations of AI, it could expand on ethical considerations in more detail, such as patient data privacy, potential biases in AI algorithms, and the need for human oversight in clinical decision-making.

Illustrations: Authors are suggested to include images of good resolution specially, figure number 1.

Conclusion:

The article “Contemporary Role and Applications of Artificial Intelligence in Dentistry”, offers a promising exploration of AI's impact on dentistry.However, it could be enhanced by addressing topics such as patient data privacy, potential biases in AI algorithms, and the essential role of human oversight in clinical decision-making.The content of the article is not only informative but also relevant, especially for readers who are starting research in the field of dentistry, therefore, I wholeheartedly recommend accepting this manuscript for publication with reservations outlined above.

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Yes
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Dental image processing, Deep learning, AI

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Respond or Comment

Views

14

Reviewer Report 26 Oct 2023

Hemant Sawhney, Sharda University, Noida, Uttar Pradesh, India

Approved

https://doi.org/10.5256/f1000research.153536.r212091

Summary:

The manuscript titled "Contemporary Role and Applications of Artificial Intelligence in Dentistry" provides a comprehensive overview of the current state of artificial intelligence (AI) in the field of dentistry. The author discusses various applications of AI, ... Continue reading

Summary:

The manuscript titled "Contemporary Role and Applications of Artificial Intelligence in Dentistry" provides a comprehensive overview of the current state of artificial intelligence (AI) in the field of dentistry. The author discusses various applications of AI, from diagnosis and treatment planning to predictive analytics, virtual consultations, and the use of robotics in dental surgery. Overall, the paper is well-structured and informative, making a valuable contribution to the field of AI in dentistry.

General Comments:

Clarity and Organization: The manuscript is well-organized, with a clear introduction and structured sections that make it easy for readers to follow the discussion.
Citations and References: The paper adequately cites relevant sources to support the discussed points.
Depth of Coverage: The paper covers a wide range of topics related to AI in dentistry, providing a comprehensive overview. However, it might be beneficial to delve deeper into specific case studies or examples of successful AI applications in dentistry to offer a more practical understanding for readers.
Technical Accuracy: The manuscript appears to be technically accurate, and the concepts related to AI are well-explained. It would be helpful if the author could provide a glossary or definitions for any technical terms that might be unfamiliar to the general reader.

Specific Comments:

Case Studies and Examples: The manuscript could benefit from the inclusion of specific case studies or real-world examples of AI applications in dentistry. This would illustrate the practical benefits of AI to the readers.
Ethical and Legal Considerations: It would be valuable to address the ethical and legal considerations surrounding AI in dentistry, such as patient data privacy, liability, and informed consent. This would provide a more well-rounded perspective on the topic.
Future Trends: Discussing future trends and potential advancements in AI within the field of dentistry could be an excellent addition. This would give the paper a forward-looking perspective and help readers understand the evolving landscape.

Conclusion:

The manuscript, "Contemporary Role and Applications of Artificial Intelligence in Dentistry," provides a valuable and informative overview of AI applications in dentistry. To enhance the paper, I recommend including case studies, addressing ethical considerations and discussing future trends. With these improvements, the manuscript will offer a more comprehensive understanding of the topic.

Overall, this paper contributes to the growing body of knowledge on AI in dentistry and should be considered for publication with the suggested revisions.

Recommendation: I recommend accepting this manuscript with minor revisions.

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Yes
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Artificial Intelligence in Dentistry and Radiology, Oral Medicine, Maxillofacial Radiology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Respond or Comment

Views

27

Reviewer Report 17 Oct 2023

Reham Osman, Department of Prosthodontics, Cairo University, Giza, Giza Governorate, Egypt

Approved with Reservations

https://doi.org/10.5256/f1000research.153536.r209464

The article at hand had described the concept of AI and its different types, its application in health care sector in details and some of its applications in dental field. As well, the article has highlighted the strength and weakness ... Continue reading

The article at hand had described the concept of AI and its different types, its application in health care sector in details and some of its applications in dental field. As well, the article has highlighted the strength and weakness point of technology and came up with some future recommendations.

The reviewer thinks that a huge effort has been made in writing this review and that it is recommended for publication after addressing all of the below mentioned points.

The title “Application of AI in Dentistry”

Different paragraphs beneath this title are more towards describing and explaining the application of AI in general health sector generally rather than in dentistry as the title entails;
example “With the advent of huge scientific data, the diagnosis of human congenital abnormalities has improved. In medical research, the support vector machine (SVM), a machine-learning model, has become a typical way of analysis. SVM may be used to classify complex situations like traumatic brain damage.^60,61DL may play a crucial role in advancing cardiovascular medicine due to the requirement for precision in diagnosis and therapy.⁶² With the adoption of deep learning, it will be feasible to identify numerous cardiovascular disorders with high-precision diagnosis.⁶³ Wearable technology combined with intelligence applications can anticipate a patient's life-threatening crisis such as a stroke, allowing physicians to deliver effective, early therapies.⁵⁴ Using AI to examine viruses has boosted transitional studies in viral immune monitoring.” This paragraph should be shifted to the title above it “Artificial intelligence for health care”.

The title: State-of-the-art AI technology in Dentistry

Same comment as in previous point. The whole paragraph under this title seems to be describing more the components and different types of artificial intelligence rather than describing Art of AI in dentistry.
Reviewer would suggest that the authors rename this paragraph differently so it is not misleading to reader.

“Taking a dental impression might soon be obsolete due to advancements in intra-oral scanners and cameras. These digital impressions minimize all laboratory operations, which sharply lowers the frequency of errors and is quicker and more accurate.”

This is a very sharp statement not supported by current state of art in literature. Digital impressions are effective and comparable to conventional impressions in cases of unilateral partially dentate patients with up to 4 missing units. However, in fully edentulous cases and extensive cases crossing midlines, accuracy of digital impressions is inferior to their conventional counterparts. Digital impression acquisition is challenging in fully edentulous cases because of lack of anatomic landmarks that allow images stitching during the scan process besides mobility of soft tissue especially floor of mouth of arch during scan process of mandibular arches. All of these factors cast a shadow about the possibility of digital impression obliterating the need for conventional impression.

“Taking a dental impression may soon be a thing of the past thanks to advancements in intra-oral scanners and cameras. In addition to being quicker and more accurate,”

Same comment as mentioned in point above

The reviewer suggests that the authors either modify and rephrase this statement as per current state of literature and add necessary references and citations or totally delete it.

In image six, there is a typo error in post-appointment bullets: should be “AI Clinician evaluation”.
Resolution of image 3 & image 5 is low, and images are not clear and cannot be easily read, please re-provide with a better resolution one that could be easier read.
Under the title: “Weakness of AI in Dentistry”.
The first statement in the paragraph “Diagnostic and treatment expenses are expected to reduce due to betterment in the healthcare field by embedding AI in the system resulting in more early diagnoses of chronic diseases.” is considered an advantage rather than a disadvantage or a weakness.
The last statement in the first paragraph “However, the disadvantage of the use of AI in dentistry includes” The sentence is incomplete and it is not mentioned what is the disadvantage of using AI in Dentistry.
Under the title: “oral medicine and pathology”

The statement “Because certain oral lesions might be precancerous or cancerous, obtaining an accurate diagnosis and treating the patient appropriately.” The statement is not complete and need to be adjusted.

Can the authors please add a reference for this statement: “AI may also help alleviate labor scarcities, which have been noted and are projected to remain in many regions of the world, therefore assisting in achieving the sustainable development goals defined by WHO.”
This Statement “AI may also help alleviate labor scarcities, which have been noted and are projected to remain in many regions of the world” under the weakness of artificial intelligence title is the opposite of increasing the unemployment which is mentioned in Figure 8 and which is properly described as one of AI weakness in health care sector.
Under the title: “AI application in 3D digital dentistry”

“Using a digitalized dental system brings real advantages in accuracy and quality, is time-consuming, and decreases costs.” Can the authors please check this sentence that one of the advantages of digital dentistry is time consumption?

“This provides extremely effective quality management. It enhanced productivity significantly and changed dental laboratories from manufacturers into sophisticated computerized production centers. Increased productivity.” Please delete increased productivity as it does not form a comprehensible sentence.
“Lastly, CAD/CAM technology has enabled the high-precision machining of novel materials such as high-performance ceramics and titanium.” Can the authors please add a reference for this statement.

FINAL GENERAL RECOMMENDATION: that the authors alter the title of paper into “Contemporary Role and Applications of Artificial Intelligence in Health care sector with specific interest in dental applications”. It is in the reviewer’s opinion will better fit the content of the paper more than the current title. Further, the applications of AI in different fields of dentistry are very briefly described considering

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Partly
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Prosthodontics, Oral implanvology, Digital Dentistry, 3D- printing, Biomechanics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Respond or Comment

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 20 Sep 2023

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1	2	3
Version 1 20 Sep 23	read	read	read

Reham Osman, Cairo University, Giza, Egypt
Hemant Sawhney, Sharda University, Noida, India
Dipmala Salunke, JSPM’s Rajarshi Shahu College of Engineering, Pune, India

Comments on this article

All Comments(0)

Add a comment

Sign up for content alerts

Browse by related subjects

Back to all reports

Reviewer Report

16 Views

07 Nov 2023 | for Version 1

Dipmala Salunke, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India

16 Views Cite this report Responses(0)

Approved With Reservations

Summary of the article:

The article provides a detailed exploration of the role of Artificial Intelligence (AI) in dentistry, its potential applications, and its limitations. It covers a range of topics, including AI algorithms, the pros and cons of using AI in dental specialties, and the need for AI to complement rather than replace dental professionals. The article also discusses the integration of AI with augmented and virtual reality technologies in dentistry.

Strengths of the Article:

Comprehensive Coverage: The article comprehensively explores the potential applications of AI in various dental specialties and discusses both the advantages and limitations of AI in the field.

Clarity of Purpose: The article clearly states its purpose, which is to assess the advancement of AI algorithms in dentistry and their performance in diagnosis, clinical decision-making, treatment, and prognosis prediction.

Integration with Emerging Technologies: The discussion of AI's integration with augmented reality and virtual reality is valuable, as it highlights innovative approaches to dental education and surgical planning.

Real-World Applications: The mention of AI's potential benefits in clinical solutions and dental laboratories demonstrates the practical advantages of AI in the dental field, such as data sharing, accuracy, and cost savings.

Areas for Improvement:

Ethical Considerations: While the article touches on the limitations of AI, it could expand on ethical considerations in more detail, such as patient data privacy, potential biases in AI algorithms, and the need for human oversight in clinical decision-making.

Illustrations: Authors are suggested to include images of good resolution specially, figure number 1.

Conclusion:

The article “Contemporary Role and Applications of Artificial Intelligence in Dentistry”, offers a promising exploration of AI's impact on dentistry.However, it could be enhanced by addressing topics such as patient data privacy, potential biases in AI algorithms, and the essential role of human oversight in clinical decision-making.The content of the article is not only informative but also relevant, especially for readers who are starting research in the field of dentistry, therefore, I wholeheartedly recommend accepting this manuscript for publication with reservations outlined above.

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Yes
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Dental image processing, Deep learning, AI

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

14 Views

26 Oct 2023 | for Version 1

Hemant Sawhney, Sharda University, Noida, Uttar Pradesh, India

14 Views Cite this report Responses(0)

Approved

Summary:

The manuscript titled "Contemporary Role and Applications of Artificial Intelligence in Dentistry" provides a comprehensive overview of the current state of artificial intelligence (AI) in the field of dentistry. The author discusses various applications of AI, from diagnosis and treatment planning to predictive analytics, virtual consultations, and the use of robotics in dental surgery. Overall, the paper is well-structured and informative, making a valuable contribution to the field of AI in dentistry.

General Comments:

Clarity and Organization: The manuscript is well-organized, with a clear introduction and structured sections that make it easy for readers to follow the discussion.
Citations and References: The paper adequately cites relevant sources to support the discussed points.
Depth of Coverage: The paper covers a wide range of topics related to AI in dentistry, providing a comprehensive overview. However, it might be beneficial to delve deeper into specific case studies or examples of successful AI applications in dentistry to offer a more practical understanding for readers.
Technical Accuracy: The manuscript appears to be technically accurate, and the concepts related to AI are well-explained. It would be helpful if the author could provide a glossary or definitions for any technical terms that might be unfamiliar to the general reader.

Specific Comments:

Case Studies and Examples: The manuscript could benefit from the inclusion of specific case studies or real-world examples of AI applications in dentistry. This would illustrate the practical benefits of AI to the readers.
Ethical and Legal Considerations: It would be valuable to address the ethical and legal considerations surrounding AI in dentistry, such as patient data privacy, liability, and informed consent. This would provide a more well-rounded perspective on the topic.
Future Trends: Discussing future trends and potential advancements in AI within the field of dentistry could be an excellent addition. This would give the paper a forward-looking perspective and help readers understand the evolving landscape.

Conclusion:

The manuscript, "Contemporary Role and Applications of Artificial Intelligence in Dentistry," provides a valuable and informative overview of AI applications in dentistry. To enhance the paper, I recommend including case studies, addressing ethical considerations and discussing future trends. With these improvements, the manuscript will offer a more comprehensive understanding of the topic.

Overall, this paper contributes to the growing body of knowledge on AI in dentistry and should be considered for publication with the suggested revisions.

Recommendation: I recommend accepting this manuscript with minor revisions.

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Yes
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Artificial Intelligence in Dentistry and Radiology, Oral Medicine, Maxillofacial Radiology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

27 Views

17 Oct 2023 | for Version 1

Reham Osman, Department of Prosthodontics, Cairo University, Giza, Giza Governorate, Egypt

27 Views Cite this report Responses(0)

Approved With Reservations

The article at hand had described the concept of AI and its different types, its application in health care sector in details and some of its applications in dental field. As well, the article has highlighted the strength and weakness point of technology and came up with some future recommendations.

The reviewer thinks that a huge effort has been made in writing this review and that it is recommended for publication after addressing all of the below mentioned points.

The title “Application of AI in Dentistry”

Different paragraphs beneath this title are more towards describing and explaining the application of AI in general health sector generally rather than in dentistry as the title entails;
example “With the advent of huge scientific data, the diagnosis of human congenital abnormalities has improved. In medical research, the support vector machine (SVM), a machine-learning model, has become a typical way of analysis. SVM may be used to classify complex situations like traumatic brain damage.^60,61DL may play a crucial role in advancing cardiovascular medicine due to the requirement for precision in diagnosis and therapy.⁶² With the adoption of deep learning, it will be feasible to identify numerous cardiovascular disorders with high-precision diagnosis.⁶³ Wearable technology combined with intelligence applications can anticipate a patient's life-threatening crisis such as a stroke, allowing physicians to deliver effective, early therapies.⁵⁴ Using AI to examine viruses has boosted transitional studies in viral immune monitoring.” This paragraph should be shifted to the title above it “Artificial intelligence for health care”.

The title: State-of-the-art AI technology in Dentistry

Same comment as in previous point. The whole paragraph under this title seems to be describing more the components and different types of artificial intelligence rather than describing Art of AI in dentistry.
Reviewer would suggest that the authors rename this paragraph differently so it is not misleading to reader.

“Taking a dental impression might soon be obsolete due to advancements in intra-oral scanners and cameras. These digital impressions minimize all laboratory operations, which sharply lowers the frequency of errors and is quicker and more accurate.”

This is a very sharp statement not supported by current state of art in literature. Digital impressions are effective and comparable to conventional impressions in cases of unilateral partially dentate patients with up to 4 missing units. However, in fully edentulous cases and extensive cases crossing midlines, accuracy of digital impressions is inferior to their conventional counterparts. Digital impression acquisition is challenging in fully edentulous cases because of lack of anatomic landmarks that allow images stitching during the scan process besides mobility of soft tissue especially floor of mouth of arch during scan process of mandibular arches. All of these factors cast a shadow about the possibility of digital impression obliterating the need for conventional impression.

“Taking a dental impression may soon be a thing of the past thanks to advancements in intra-oral scanners and cameras. In addition to being quicker and more accurate,”

Same comment as mentioned in point above

The reviewer suggests that the authors either modify and rephrase this statement as per current state of literature and add necessary references and citations or totally delete it.

In image six, there is a typo error in post-appointment bullets: should be “AI Clinician evaluation”.
Resolution of image 3 & image 5 is low, and images are not clear and cannot be easily read, please re-provide with a better resolution one that could be easier read.
Under the title: “Weakness of AI in Dentistry”.
The first statement in the paragraph “Diagnostic and treatment expenses are expected to reduce due to betterment in the healthcare field by embedding AI in the system resulting in more early diagnoses of chronic diseases.” is considered an advantage rather than a disadvantage or a weakness.
The last statement in the first paragraph “However, the disadvantage of the use of AI in dentistry includes” The sentence is incomplete and it is not mentioned what is the disadvantage of using AI in Dentistry.
Under the title: “oral medicine and pathology”

The statement “Because certain oral lesions might be precancerous or cancerous, obtaining an accurate diagnosis and treating the patient appropriately.” The statement is not complete and need to be adjusted.

Can the authors please add a reference for this statement: “AI may also help alleviate labor scarcities, which have been noted and are projected to remain in many regions of the world, therefore assisting in achieving the sustainable development goals defined by WHO.”
This Statement “AI may also help alleviate labor scarcities, which have been noted and are projected to remain in many regions of the world” under the weakness of artificial intelligence title is the opposite of increasing the unemployment which is mentioned in Figure 8 and which is properly described as one of AI weakness in health care sector.
Under the title: “AI application in 3D digital dentistry”

“Using a digitalized dental system brings real advantages in accuracy and quality, is time-consuming, and decreases costs.” Can the authors please check this sentence that one of the advantages of digital dentistry is time consumption?

“This provides extremely effective quality management. It enhanced productivity significantly and changed dental laboratories from manufacturers into sophisticated computerized production centers. Increased productivity.” Please delete increased productivity as it does not form a comprehensible sentence.
“Lastly, CAD/CAM technology has enabled the high-precision machining of novel materials such as high-performance ceramics and titanium.” Can the authors please add a reference for this statement.

FINAL GENERAL RECOMMENDATION: that the authors alter the title of paper into “Contemporary Role and Applications of Artificial Intelligence in Health care sector with specific interest in dental applications”. It is in the reviewer’s opinion will better fit the content of the paper more than the current title. Further, the applications of AI in different fields of dentistry are very briefly described considering

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes
Are all factual statements correct and adequately supported by citations?

Partly
Is the review written in accessible language?

Yes
Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Prosthodontics, Oral implanvology, Digital Dentistry, 3D- printing, Biomechanics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] 1. Kong S-C, Cheung WM-Y, Zhang G: Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports. 2022; 7: 100223. Publisher Full Text

[2] 2. Obaideen K, Olabi AG, Al Swailmeen Y, et al.: Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs). Sustainability. 2023; 15(2): 1418. Publisher Full Text

[3] 3. Stranieri A, Venkatraman S, Minicz J, et al.: Emerging point of care devices and artificial intelligence: Prospects and challenges for public health. Smart Health. 2022/06/01 2022; 24: 100279. Publisher Full Text

[4] 4. Mamun Q: Blockchain technology in the future of healthcare. Smart Health. 2022/03/012022; 23: 100223. Publisher Full Text

[5] 5. Pourhomayoun M, Shakibi M: Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021/04/012021; 20: 100178. Publisher Full Text

[6] 6. Rahman MJ, Morshed BI, Harmon B, et al.: A pilot study towards a smart-health framework to collect and analyze biomarkers with low-cost and flexible wearables. Smart Health. 2022/03/012022; 23: 100249. Publisher Full Text

[7] 7. Abdelkareem MA, Soudan B, Mahmoud MS, et al.: Progress of artificial neural networks applications in hydrogen production. Chem. Eng. Res. Des. 2022; 182: 66–86. Publisher Full Text

[8] 8. Salameh T, Kumar PP, Olabi A, et al.: Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals. Journal of Energy Storage. 2022; 55: 105312. Publisher Full Text

[9] 9. Guo K, Yang Z, Yu C-H, et al.: Artificial intelligence and machine learning in design of mechanical materials. Materials Horizons. 2021; 8(4): 1153–1172. Publisher Full Text

[10] 10. Hashimoto DA, Rosman G, Rus D, et al.: Artificial intelligence in surgery: promises and perils. Ann. Surg. 2018; 268(1): 70–76. Publisher Full Text

[11] 11. Kaziha O, Bonny T: A comparison of quantized convolutional and lstm recurrent neural network models using mnist. IEEE. 2019; 1–5.

[12] 12. Fiske A, Henningsen P, Buyx A: Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J. Med. Internet Res. 2019; 21(5): e13216. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Siau K, Wang W: Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM). 2020; 31(2): 74–87. Publisher Full Text

[14] 14. Vrontis D, Christofi M, Pereira V, et al.: Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Int. J. Hum. Resour. Manag. 2022; 33(6): 1237–1266. Publisher Full Text

[15] 15. Patil R, Kumar S: Bibliometric survey on diagnosis of plant leaf diseases using artificial intelligence. International Journal of Modern Agriculture. 2020; 9(3): 1111–1131.

[16] 16. van Hoek J , Huber A, Leichtle A, et al.: A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur. J. Radiol. 2019; 121: 108742. PubMed Abstract | Publisher Full Text

[17] 17. Al Nassan W, Bonny T, Obaideen K: A Customized Convolutional Neural Network for Dental Bitewing Images Segmentation. presented at: The International Conference on Electrical and Computing Technologies and Applications, 2022 (ICECTA’2022) 2022.

[18] 18. Al Nassan W, Bonny T, Obaideen K, et al.: AN LSTM model-based Prediction of Chaotic System: Analyzing the Impact of Training Dataset Precision on the Performance. IEEE.2022; 337–342.

[19] 19. Shehata N, Obaideen K, Sayed ET, et al.: Role of Refuse-derived fuel in circular economy and sustainable development goals. Process Saf. Environ. Prot. 2022; 163: 558–573. Publisher Full Text

[20] 20. Vinuesa R, Azizpour H, Leite I, et al.: The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020; 11(1): 1–10.

[21] 21. Fernandez RM: SDG3 good health and well-being: integration and connection with other SDGs. Good Health and Well-Being. 2020; 629–636. Publisher Full Text

[22] 22. Murphy K, Di Ruggiero E, Upshur R, et al.: Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med. Ethics. 2021; 22(1): 1–17.

[23] 23. Shan T, Tay F, Gu L: Application of artificial intelligence in dentistry. J. Dent. Res. 2021; 100(3): 232–244. Publisher Full Text

[24] 24. Fadzly N, Zuharah WF, Jenn Ney JW: Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees. Plant Signal. Behav. 2021; 16(10): 1935605. PubMed Abstract | Publisher Full Text | Free Full Text

[25] 25. Hung KF, Ai QYH, King AD, et al.: Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network. Clin. Oral Investig. 2022; 26(5): 3987–3998. PubMed Abstract | Publisher Full Text

[26] 26. Achararit P, Manaspon C, Jongwannasiri C, et al.: Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks. European Journal of Dentistry. 2023. Publisher Full Text

[27] 27. Chen Y-w, Stanley K, Att W: Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020; 51(3): 248–257.

[28] 28. Reddy MS, Shetty SR, Vannala V: Embracing personalized medicine in dentistry. J. Pharm. Bioallied Sci. 2019; 11(Suppl 2): S92–S96. PubMed Abstract | Publisher Full Text

[29] 29. Nijman S, Leeuwenberg A, Beekers I, et al.: Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review. J. Clin. Epidemiol. 2022; 142: 218–229. Publisher Full Text

[30] 30. Lahoud P, Jacobs R, Boisse P, et al.: Precision medicine using patient-specific modelling: State of the art and perspectives in dental practice. Clin. Oral Investig. 2022; 26(8): 5117–5128. PubMed Abstract | Publisher Full Text

[31] 31. Fatima A, Shafi I, Afzal H, et al.: Advancements in dentistry with artificial intelligence: current clinical applications and future perspectives. MDPI. 2022; 10: 2188. Publisher Full Text

[32] 32. Khanagar SB, Al-Ehaideb A, Maganur PC, et al.: Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of dental sciences. 2021; 16(1): 508–522. PubMed Abstract | Publisher Full Text | Free Full Text

[33] 33. Tandon D, Rajawat J, Banerjee M: Present and future of artificial intelligence in dentistry. Journal of oral biology and craniofacial research. 2020; 10(4): 391–396. Publisher Full Text

[34] 34. Schwendicke F, Samek W, Krois J: Artificial intelligence in dentistry: chances and challenges. J. Dent. Res. 2020; 99(7): 769–774. PubMed Abstract | Publisher Full Text | Free Full Text

[35] 35. Bindushree V, Sameen R, Vasudevan V, et al.: Artificial intelligence: In modern dentistry. Journal of Dental Research and Review. 2020; 7(1): 27.

[36] 36. Bi Q, Goodman KE, Kaminsky J, et al.: What is machine learning? A primer for the epidemiologist. Am. J. Epidemiol. 2019; 188(12): 2222–2239.

[37] 37. Khamparia A, Singh KM: A systematic review on deep learning architectures and applications. Expert. Syst. 2019; 36(3): e12400. Publisher Full Text

[38] 38. Grollmisch S, Cano E, Kehling C, et al.: Analyzing the potential of pre-trained embeddings for audio classification tasks. IEEE. 2021; 790–794.

[39] 39. do Nascimento Gerhardt M , Fontenele RC, Leite AF, et al.: Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks. J. Dent. 2022; 122: 104139. PubMed Abstract | Publisher Full Text

[40] 40. Mahmoud S, Irwin B, Chekmarev D, et al.: Imputation of sensory properties using deep learning. J. Comput. Aided Mol. Des. 2021; 35(11): 1125–1140. Publisher Full Text

[41] 41. Tian H, Chen S-C, Shyu M-L: Evolutionary programming based deep learning feature selection and network construction for visual data classification. Inf. Syst. Front. 2020; 22(5): 1053–1066. Publisher Full Text

[42] 42. Zhou X-Y, Guo Y, Shen M, et al.: Application of artificial intelligence in surgery. Front. Med. 2020; 14(4): 417–430. PubMed Abstract | Publisher Full Text

[43] 43. Shen J, Zhang CJ, Jiang B, et al.: Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med. Inform. 2019; 7(3): e10010. PubMed Abstract | Publisher Full Text | Free Full Text

[44] 44. Yamada M, Saito Y, Imaoka H, et al.: Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci. Rep. 2019; 9(1): 1–9.

[45] 45. Mishra A, Yadav P, Kim S: Artificial Intelligence Accelerators. Artificial Intelligence and Hardware Accelerators. Springer; 2023; 1–52.

[46] 46. Bonny T, Henkel J: Instruction splitting for efficient code compression.2007; 646–651.

[47] 47. Bonny T, Haq A: Emulation of high-performance correlation-based quantum clustering algorithm for two-dimensional data on FPGA. Quantum Inf. Process. 2020; 19: 1–21.

[48] 48. Bonny T, Henkel J: Using Lin-Kernighan Algorithm for Look-up Table compression to improve code density.2006; 259–265.

[49] 49. Sambas A, Vaidyanathan S, Zhang X, et al.: A Novel 3D Chaotic System with Line Equilibrium: Multistability, Integral Sliding Mode Control, Electronic Circuit, FPGA and its Image Encryption. IEEE Access. 2022; 10: 68057–68074. Publisher Full Text

[50] 50. Bonny T: Chaotic or Hyper-chaotic Oscillator? Numerical Solution, Circuit Design, MATLAB HDL-Coder Implementation, VHDL Code, Security Analysis, and FPGA Realization. Circuits, Systems, and Signal Processing. 2021/03/01 2021; 40(3): 1061–1088. Publisher Full Text

[51] 51. Bonny T, Kashkash M, Ahmed F: An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination. Desalination. 2022/01/152022; 522: 115443. Publisher Full Text

[52] 52. Punith Kumar CR, Natarajan R, Padma K, et al.: ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A BRIEF REVIEW. Suranaree Journal of Science & Technology. 2022; 29(2). Publisher Full Text

[53] 53. Grischke J, Johannsmeier L, Eich L, et al.: Dentronics: Towards robotics and artificial intelligence in dentistry. Dent. Mater. 2020/06/01/2020; 36(6): 765–778. PubMed Abstract | Publisher Full Text

[54] 54. Jiang F, Jiang Y, Zhi H, et al.: Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017; 2(4): 230–243. PubMed Abstract | Publisher Full Text | Free Full Text

[55] 55. Wen X, Leng P, Wang J, et al.: Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC bioinformatics. 2022; 23(1): 1–20. Publisher Full Text

[56] 56. Thrall JH, Li X, Li Q, et al.: Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J. Am. Coll. Radiol. 2018; 15(3): 504–508. PubMed Abstract | Publisher Full Text

[57] 57. Kaziha O, Jarndal A, Bonny T: Genetic Algorithm Augmented Convolutional Neural Network for Image Recognition Applications. IEEE. 2020; 1–5.

[58] 58. Nasir N, Kansal A, Barneih F, et al.: Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. Intelligent Systems with Applications. 2023; 17: 200160. Publisher Full Text

[59] 59. Barneih F, Nasir N, Alshaltone O, et al.: Artificial neural network model using short-term fourier transform for epilepsy seizure detection. IEEE. 2022; 1–5.

[60] 60. Orrù G, Pettersson-Yeo W, Marquand AF, et al.: Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. Apr 2012; 36(4): 1140–1152. PubMed Abstract | Publisher Full Text

[61] 61. Nasir N, Oswald P, Barneih F, et al.: Hypertension classification using machine learning part II. IEEE. 2021; 459–463.

[62] 62. Fiandaca MS, Mapstone M, Mahmoodi A, et al.: Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls. PLoS One. 2018; 13(4): e0195318. PubMed Abstract | Publisher Full Text | Free Full Text

[63] 63. Manne R, Kantheti SC: Application of artificial intelligence in healthcare: chances and challenges. Current Journal of Applied Science and Technology. 2021; 40(6): 78–89. Publisher Full Text

[64] 64. Chiappelli F, Balenton N, Khakshooy A: Future innovations in viral immune surveillance: a novel place for bioinformation and artificial intelligence in the administration of health care. Bioinformation. 2018; 14(5): 201–205. PubMed Abstract | Publisher Full Text | Free Full Text

[65] 65. Surovková J, Haluzová S, Strunga M, et al.: The New Role of the Dental Assistant and Nurse in the Age of Advanced Artificial Intelligence in Telehealth Orthodontic Care with Dental Monitoring: Preliminary Report. Appl. Sci. 2023; 13(8): 5212. Publisher Full Text

[66] 66. Lahoud P, Diels S, Niclaes L, et al.: Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT. J. Dent. 2022/01/01 2022; 116: 103891. Publisher Full Text

[67] 67. Engels P, Meyer O, Schönewolf J, et al.: Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs. J. Dent. 2022/06/01 2022; 121: 104124. PubMed Abstract | Publisher Full Text

[68] 68. Fontenele RC, Gerhardt MN, Pinto JC, et al.: Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images – A validation study. J. Dent. 2022/04/01 2022; 119: 104069. PubMed Abstract | Publisher Full Text

[69] 69. Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al.: Deep learning for caries detection: A systematic review. J. Dent. 2022/07/01 2022; 122: 104115. Publisher Full Text

[70] 70. Haidar ZS: Digital Dentistry: Past, Present, and Future. Digital Medicine and Healthcare Technology. 2023; 2. Publisher Full Text

[71] 71. Karantanas AH, Efremidis S: The concept of the invisible radiologist in the era of artificial intelligence. Eur. J. Radiol. 2022; 155: 110147. PubMed Abstract | Publisher Full Text

[72] 72. Kim T, Cha M, Kim H, et al.: Learning to discover cross-domain relations with generative adversarial networks. PMLR. 2017; 1857–1865.

[73] 73. Asiri AF, Altuwalah AS: The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review. The Saudi Dental Journal. 2022; 34: 270–281. PubMed Abstract | Publisher Full Text | Free Full Text

[74] 74. Naylor CD: On the prospects for a (deep) learning health care system. JAMA. 2018; 320(11): 1099–1100. PubMed Abstract | Publisher Full Text

[75] 75. Rokhshad R, Keyhan SO, Yousefi P: Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review. Maxillofacial Plastic and Reconstructive Surgery. 2023; 45(1): 14. PubMed Abstract | Publisher Full Text | Free Full Text

[76] 76. Nielsen MA: Neural networks and deep learning. San Francisco, CA, USA: Determination press; 2015; vol. 25. .

[77] 77. Cao C, Cerfolio RJ: Virtual or augmented reality to enhance surgical education and surgical planning. Thorac. Surg. Clin. 2019; 29(3): 329–337. Publisher Full Text

[78] 78. Durham M, Engel B, Ferrill T, et al.: Digitally augmented learning in implant dentistry. Oral and Maxillofacial Surgery Clinics. 2019; 31(3): 387–398. PubMed Abstract | Publisher Full Text

[79] 79. Kikuchi H, Ikeda M, Araki K: Evaluation of a virtual reality simulation system for porcelain fused to metal crown preparation at Tokyo Medical and Dental University. J. Dent. Educ. 2013; 77(6): 782–792. Publisher Full Text

[80] 80. Khanna SS, Dhaimade PA: Artificial intelligence: transforming dentistry today. Indian J. Basic. Appl. Med. Res. 2017; 6(3): 161–167.

[81] 81. Wiederhold MD, Gao K, Wiederhold BK: Clinical use of virtual reality distraction system to reduce anxiety and pain in dental procedures. Cyberpsychol. Behav. Soc. Netw. 2014; 17(6): 359–365. PubMed Abstract | Publisher Full Text | Free Full Text

[82] 82. Joda T, Gallucci G, Wismeijer D, et al.: Augmented and virtual reality in dental medicine: A systematic review. Comput. Biol. Med. 2019; 108: 93–100. Publisher Full Text

[83] 83. Dutã M, Amariei CI, Bogdan CM, et al.: An overview of virtual and augmented reality in dental education. Oral. Health Dent. Manag. 2011; 10: 42–49.

[84] 84. Kazi H, Haddawy P, Suebnukarn S: Leveraging a domain ontology to increase the quality of feedback in an intelligent tutoring system. Springer; 2010; 75–84.

[85] 85. Park WJ, Park J-B: History and application of artificial neural networks in dentistry. European journal of dentistry. 2018; 12(04): 594–601. Publisher Full Text

[86] 86. Tamaki Y, Nomura Y, Katsumura S, et al.: Construction of a dental caries prediction model by data mining. J. Oral Sci. 2009; 51(1): 61–68. PubMed Abstract | Publisher Full Text

[87] 87. Kumagai A, Jeong S, Kim D, et al.: Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults. Sci. Rep. 2023; 13(1): 726. PubMed Abstract | Publisher Full Text | Free Full Text

[88] 88. Association CD: Dental Specialties. CDSA ACSD;

[89] 89. Rekow ED: Digital dentistry: The new state of the art—Is it disruptive or destructive? Dent. Mater. 2020; 36(1): 9–24. Publisher Full Text

[90] 90. Sharma H, Suprabha BS, Rao A: Teledentistry and its applications in paediatric dentistry: A literature review. Pediatr. Dent. J. 2021; 31(3): 203–215. PubMed Abstract | Publisher Full Text | Free Full Text

[91] 91. Gleeson H, Kalsi A: Remote clinical consultations in restorative dentistry—A survey. J. Dent. 2022; 117: 103914. Publisher Full Text

[92] 92. Kannan P: Artificial Intelligence. Applications in healthcare, Asian hospital and healthcare management. 2019; 19.

[93] 93. Esmaeili MA: New chapter in healthcare environmental impact reduction through medical treatment choices: The case of diagnostic imaging services. Wichita State University; 2016.

[94] 94. Tong W, Nugent S, Jensen G, et al.: An algorithm for locating landmarks on dental X-rays. IEEE. 1989; 552–554.

[95] 95. Yeager D: What Will Happen When Artificial Intelligence Comes to Radiology? Radiology Today. 2016; 17(5): 12.

[96] 96. Xie X, Wang L, Wang A: Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010; 80(2): 262–266. PubMed Abstract | Publisher Full Text | Free Full Text

[97] 97. Kattadiyil MT, Mursic Z, AlRumaih H, et al.: Intraoral scanning of hard and soft tissues for partial removable dental prosthesis fabrication. J. Prosthet. Dent. 2014; 112(3): 444–448. PubMed Abstract | Publisher Full Text

[98] 98. Vera V, Corchado E, Redondo R, et al.: Applying soft computing techniques to optimise a dental milling process. Neurocomputing. 2013; 109: 94–104. Publisher Full Text

[99] 99. Zakirulla M, Javed S, Assiri NEH, et al.: An overview of artificial neural network in the field of pediatric dentistry. Journal of Dental and Orofacial Research. 2020; 16(1): 20–25.

[100] 100. Seol Y-J, Kang H-W, Lee SJ, et al.: Bioprinting technology and its applications. Eur. J. Cardiothorac. Surg. 2014; 46(3): 342–348. Publisher Full Text

[101] 101. Zhu Y, Xu T, Peng L, et al.: Faster-RCNN based intelligent detection and localization of dental caries. Displays. 2022; 74: 102201. Publisher Full Text

[102] 102. Shaheen E, Leite A, Alqahtani KA, et al.: A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. J. Dent. 2021/12/012021; 115: 103865. PubMed Abstract | Publisher Full Text

[103] 103. Umer F, Habib S: Critical analysis of artificial intelligence in endodontics: a scoping review. J. Endod. 2021; 48: 152–160. Publisher Full Text

[104] 104. Hiraiwa T, Ariji Y, Fukuda M, et al.: A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 2019; 48(3): 20180218. PubMed Abstract | Publisher Full Text | Free Full Text

[105] 105. Qu Y, Lin Z, Yang Z, et al.: Machine learning models for prognosis prediction in endodontic microsurgery. J. Dent. 2022/03/012022; 118: 103947. PubMed Abstract | Publisher Full Text

[106] 106. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al.: Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. Journal of dental sciences. 2021; 16(1): 482–492. PubMed Abstract | Publisher Full Text | Free Full Text

[107] 107. Ter Horst R, van Weert H , Loonen T, et al.: Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning. J. Cranio-Maxillofac. Surg. 2021; 49(9): 775–782. PubMed Abstract | Publisher Full Text

[108] 108. Jung S-K, Kim T-W: New approach for the diagnosis of extractions with neural network machine learning. Am. J. Orthod. Dentofac. Orthop. 2016; 149(1): 127–133. Publisher Full Text

[109] 109. Gandedkar NH, Wong MT, Darendeliler MA: Role of Virtual Reality (VR), Augmented Reality (AR) and Artificial Intelligence (AI) in tertiary education and research of orthodontics: An insight. Elsevier; 2021; 69–77.

[110] 110. Mohammad-Rahimi H, Nadimi M, Rohban MH, et al.: Machine learning and orthodontics, current trends and the future opportunities: a scoping review. Am. J. Orthod. Dentofac. Orthop. 2021; 160(2): 170–192.e4. Publisher Full Text

[111] 111. Wu T-H, Lian C, Lee S, et al.: Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE Trans. Med. Imaging. 2022; 41(11): 3158–3166. PubMed Abstract | Publisher Full Text

[112] 112. Wang H, Minnema J, Batenburg KJ, et al.: Multiclass CBCT image segmentation for orthodontics with deep learning. J. Dent. Res. 2021; 100(9): 943–949. PubMed Abstract | Publisher Full Text | Free Full Text

[113] 113. Revilla-León M, Gómez-Polo M, Barmak AB, et al.: Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J. Prosthet. Dent. 2022. PubMed Abstract | Publisher Full Text

[114] 114. Revilla-León M, Gómez-Polo M, Vyas S, et al.: Artificial intelligence models for tooth-supported fixed and removable prosthodontics: a systematic review. J. Prosthet. Dent. 2021.

[115] 115. Nguyen TT, Larrivée N, Lee A, et al.: Use of artificial intelligence in dentistry: current clinical trends and research advances. J. Can. Dent. Assoc. 2021; 87(l7): 1488–2159.

[116] 116. Tadinada A: Artificial intelligence, machine learning, and the human interface in medicine: Is there a sweet spot for oral and maxillofacial radiology? Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2019/04/01/2019; 127(4): 265–266. PubMed Abstract | Publisher Full Text

[117] 117. Tuzoff DV, Tuzova LN, Bornstein MM, et al.: Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology. 2019; 48(4): 20180051. PubMed Abstract | Publisher Full Text | Free Full Text

[118] 118. Brignardello-Petersen R: Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images. J. Am. Dent. Assoc. 2020; 151(9): e83. PubMed Abstract | Publisher Full Text

[119] 119. Uysal S, Akcicek G, Yalcin ED, et al.: The influence of voxel size and artifact reduction on the detection of vertical root fracture in endodontically treated teeth. Acta Odontol. Scand. 2021; 79(5): 354–358. Publisher Full Text

[120] 120. Queiroz PM, Groppo FC, Oliveira ML, et al.: Evaluation of the efficacy of a metal artifact reduction algorithm in different cone beam computed tomography scanning parameters. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2017; 123(6): 729–734. PubMed Abstract | Publisher Full Text

[121] 121. Al-Rawi N, Sultan A, Rajai B, et al.: The Effectiveness of Artificial Intelligence in Detection of Oral Cancer. Int. Dent. J. 2022; 72: 436–447. PubMed Abstract | Publisher Full Text | Free Full Text

[122] 122. Dunkel L, Fernandez-Luque L, Loche S, et al.: Digital technologies to improve the precision of paediatric growth disorder diagnosis and management. Growth Hormon. IGF Res. 2021; 59: 101408. PubMed Abstract | Publisher Full Text

[123] 123. Al-Dwairi ZN, Al Haj Ebrahim AA, Baba NZ: A Comparison of the Surface and Mechanical Properties of 3D Printable Denture-Base Resin Material and Conventional Polymethylmethacrylate (PMMA). J. Prosthodont. 2022; 32: 40–48. Publisher Full Text

[124] 124. Al Hamad KQ, Al Quran FA, Jwaied SZ, et al.: Effect of CAD/CAM Bur Deterioration on the Surface Roughness of Ceramic Crowns. J. Prosthodont. 2022; 31(4): 320–325. PubMed Abstract | Publisher Full Text

[125] 125. Al-Dwairi ZN, Tahboub KY, Baba NZ, et al.: A Comparison of the Flexural and Impact Strengths and Flexural Modulus of CAD/CAM and Conventional Heat-Cured Polymethyl Methacrylate (PMMA). J. Prosthodont. 2020; 29(4): 341–349. PubMed Abstract | Publisher Full Text

[126] 126. Olley RC, Alhaij S, Mohsen BM, et al.: Novel Confocal-Laser-Scanning-Microscopy and conventional measures investigating eroded dentine following dentifrice dab-on and brushing abrasion. Heliyon. 2020/03/01/2020; 6(3): e03282. PubMed Abstract | Publisher Full Text | Free Full Text

[127] 127. Mangano FG, Hauschild U, Veronesi G, et al.: Trueness and precision of 5 intraoral scanners in the impressions of single and multiple implants: a comparative in vitro study. BMC Oral Health. 2019/06/06 2019; 19(1): 101. Publisher Full Text

[128] 128. Apostolakis D, Michelinakis G, Kamposiora P, et al.: The current state of computer assisted orthognathic surgery: A narrative review. J. Dent. 2022/04/01/2022; 119: 104052. PubMed Abstract | Publisher Full Text

[129] 129. Joda T, Bornstein MM, Jung RE, et al.: Recent Trends and Future Direction of Dental Research in the Digital Era. Int. J. Environ. Res. Public Health. 2020; 17(6): 1987. PubMed Abstract | Publisher Full Text | Free Full Text

[130] 130. O'Toole S, Bernabe E, Bartlett D, et al.: The influence of scanner precision and analysis software on quantifying 3D intraoral changes: Two-factor factorial experimental design. J. Med. Internet Res. 2020; 22. Publisher Full Text

[131] 131. Charalambous P, O’Toole S, Austin R, et al.: The threshold of an intra oral scanner to measure lesion depth on natural unpolished teeth. Dent. Mater. 2022; 38: 1354–1361. PubMed Abstract | Publisher Full Text | Free Full Text

[132] 132. Revilla-León M, Zeitler JM, Barmak AB, et al.: Accuracy of the 3-dimensional virtual patient representation obtained by using 4 different techniques: An in vitro study. J. Prosthet. Dent. 2022/06/27/2022. PubMed Abstract | Publisher Full Text

[133] 133. Oliveros-López L-G, Castillo-de-Oyagüe R, Serrera-Figallo M-Á, et al.: Bone Loss in Bruxist Patients Wearing Dental Implant Prostheses: A Finite Element Analysis. Metals. 2020; 10(9): 1132. Publisher Full Text

[134] 134. Al-Hassiny A: Fundamentals of Computer-Aided Design (CAD) in Dental Healthcare: From Basics to Beyond. 3D Printing in Oral Health Science: Applications and Future Directions. Springer; 2022; 93–119.

[135] 135. Israni ST, Verghese A: Humanizing artificial intelligence. JAMA. 2019; 321(1): 29–30. Publisher Full Text

[136] 136. Engelmann L: Into the Deep-AI and Total Pathology: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Topol E, editor. New York: Basic Books; 2010. 400 pp., $17.99 (paperback), ISBN 9781541644649. Taylor & Francis; 2020.

[137] 137. Schwendicke F, Mertens S, Cantu AG, et al.: Cost-effectiveness of AI for caries detection: randomized trial. J. Dent. 2022; 119: 104080. PubMed Abstract | Publisher Full Text

[138] 138. Watt RG, Daly B, Allison P, et al.: Ending the neglect of global oral health: time for radical action. Lancet. 2019; 394(10194): 261–272. PubMed Abstract | Publisher Full Text

[139] 139. He J, Baxter SL, Xu J, et al.: The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019; 25(1): 30–36. PubMed Abstract | Publisher Full Text | Free Full Text

[140] 140. Kario K, Harada N, Okura A: The first software as medical device of evidence-based hypertension digital therapeutics for clinical practice. Hypertens. Res. 2022; 45(12): 1899–1905. PubMed Abstract | Publisher Full Text | Free Full Text

[141] 141. Klonoff DC, Gutierrez A, Fleming A, et al.: Levy Diabetes Technology & Therapeutics.May 2019.

[142] 142. Redman TC: If your data is bad, your machine learning tools are useless. Harv. Bus. Rev. 2018; 2.

[143] 143. Yang J, Xie Y, Liu L, et al.: Automated dental image analysis by deep learning on small dataset. IEEE. 2018; 492–497.

[144] 144. Schwendicke F, Golla T, Dreher M, et al.: Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent. 2019; 91: 103226. PubMed Abstract | Publisher Full Text

[145] 145. Lee J-H, Jeong S-N: Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020; 99(26): e20787. Publisher Full Text

[146] 146. Pauwels R: A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral Radiol. 2021/01/01 2021; 37(1): 153–160. PubMed Abstract | Publisher Full Text

[147] 147. Lerner H, Mouhyi J, Admakin O, et al.: Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020/03/19 2020; 20(1): 80. PubMed Abstract | Publisher Full Text | Free Full Text

[148] 148. Bernauer SA, Zitzmann NU, Joda T: The use and performance of artificial intelligence in prosthodontics: a systematic review. Sensors. 2021; 21(19): 6628. PubMed Abstract | Publisher Full Text | Free Full Text

[149] 149. Hwang D, Kang SK, Kim KY, et al.: Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur. J. Nucl. Med. Mol. Imaging. 2022; 49(6): 1833–1842. Publisher Full Text

[150] 150. Qiu B, Guo J, Kraeima J, et al.: Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Phys. Med. Biol. 2019; 64(17): 175020. Publisher Full Text

[151] 151. Kwon O, Yong T-H, Kang S-R, et al.: Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofacial Radiology. 2020; 49(8): 20200185. PubMed Abstract | Publisher Full Text | Free Full Text

[152] 152. Lee J-S, Adhikari S, Liu L, et al.: Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiology. 2019; 48(1): 20170344. PubMed Abstract | Publisher Full Text | Free Full Text

[153] 153. Bichu YM, Hansa I, Bichu AY, et al.: Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog. Orthod. 2021; 22(1): 1–11.

[154] 154. Lee J-H, Kim D-h, Jeong S-N, et al.: Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science. 2018; 48(2): 114–123. PubMed Abstract | Publisher Full Text | Free Full Text

[155] 155. Thanathornwong B, Suebnukarn S: Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry. 2020; 50(2): 169–174. PubMed Abstract | Publisher Full Text | Free Full Text

[156] 156. Kim DW, Kim H, Nam W, et al.: Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018; 116: 207–214. Publisher Full Text

[157] 157. Dal Pra K, Lemos C, Okamoto R, et al.: Efficacy of the C-terminal telopeptide test in predicting the development of bisphosphonate-related osteonecrosis of the jaw: a systematic review. Int. J. Oral Maxillofac. Surg. 2017; 46(2): 151–156. PubMed Abstract | Publisher Full Text

[158] 158. Soffer S, Ben-Cohen A, Shimon O, et al.: Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019; 290(3): 590–606. Publisher Full Text

[159] 159. Zhang K, Chen H, Lyu P, et al.: A relation-based framework for effective teeth recognition on dental periapical X-rays. Comput. Med. Imaging Graph. 2022; 95: 102022. Publisher Full Text

[160] 160. Torosdagli N, Liberton DK, Verma P, et al.: Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans. Med. Imaging. 2018; 38(4): 919–931. PubMed Abstract | Publisher Full Text

[161] 161. Qaddoura R, Manaseer WA, Abushariah MA, et al.: Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer. Multimed. Tools Appl. 2020; 79(29): 22027–22045. Publisher Full Text

[162] 162. Mahdi FP, Yagi N, Kobashi S: Automatic teeth recognition in dental X-ray images using transfer learning based faster R-CNN. IEEE. 2020; 16–21.

[163] 163. Chen X, Lian C, Deng HH, et al.: Fast and Accurate Craniomaxillofacial Landmark Detection via 3D Faster R-CNN. IEEE Trans. Med. Imaging. 2021; 40(12): 3867–3878. PubMed Abstract | Publisher Full Text | Free Full Text

[164] 164. Moidu NP, Sharma S, Chawla A, et al.: Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin. Oral Investig. 2022/01/01 2022; 26(1): 651–658. PubMed Abstract | Publisher Full Text

[165] 165. Poedjiastoeti W, Suebnukarn S: Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors. hir. 07 2018; 24(3): 236–241. PubMed Abstract | Publisher Full Text | Free Full Text

[166] 166. Khanagar SB, Alfouzan K, Awawdeh M, et al.: Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. Appl. Sci. 2022; 12(11): 5581. Publisher Full Text

[167] 167. Corbella S, Srinivas S, Cabitza F: Applications of deep learning in dentistry. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2021; 132(2): 225–238. Publisher Full Text

[168] 168. Romanini J, Kanomata LM, De Figueiredo RM: ARTIFICIAL INTELLIGENCE: AID IN EARLY ORAL CANCER DIAGNOSIS AT THE DENTAL CLINIC. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2020; 130(3): e282–e283. Publisher Full Text

[169] 169. Singh NK, Raza K: Progress in deep learning-based dental and maxillofacial image analysis: A systematic review. Expert Syst. Appl. 2022; 199: 116968. Publisher Full Text

[170] 170. Lee J-H, Kim D-H, Jeong S-N, et al.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 2018; 77: 106–111. PubMed Abstract | Publisher Full Text

[171] 171. Leonardi R, Giudice AL, Farronato M, et al.: Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks. Am. J. Orthod. Dentofac. Orthop. 2021; 159(6): 824–835.e1. PubMed Abstract | Publisher Full Text

[172] 172. Gil S-M, Kim I, Cho J-H, et al.: Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am. J. Orthod. Dentofac. Orthop. 2022; 161(4): e361–e371. PubMed Abstract | Publisher Full Text

[173] 173. Amantini SNSR, Montilha AAP, Antonelli BC, et al.: Using augmented reality to motivate oral hygiene practice in children: protocol for the development of a serious game. JMIR research protocols. 2020; 9(1): e10987. PubMed Abstract | Publisher Full Text | Free Full Text

[174] 174. Zafar S, Zachar JJ: Evaluation of HoloHuman augmented reality application as a novel educational tool in dentistry. Eur. J. Dent. Educ. 2020; 24(2): 259–265. PubMed Abstract | Publisher Full Text

[175] 175. Lee J-H, Jeong S-N: Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020; 99(26): e20787–e20787. PubMed Abstract | Publisher Full Text | Free Full Text

[176] 176. Ren R, Luo H, Su C, et al.: Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ. 2021; 9: e11451. PubMed Abstract | Publisher Full Text | Free Full Text

[177] 177. Yamaguchi S, Lee C, Karaer O, et al.: Predicting the debonding of CAD/CAM composite resin crowns with AI. J. Dent. Res. 2019; 98(11): 1234–1238. PubMed Abstract | Publisher Full Text

[178] 178. Lerner H, Mouhyi J, Admakin O, et al.: Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020; 20(1): 1–16. Publisher Full Text

[179] 179. Li H, Sakai T, Tanaka A, et al.: Interpretable AI Explores Effective Components of CAD/CAM Resin Composites. J. Dent. Res. 2022; 101: 1363–1371. Publisher Full Text

[180] 180. Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al.: A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med. Imaging. 2021; 21(1): 1–9.

[181] 181. Rojek I, Mikołajewski D, Dostatni E, et al.: AI-optimized technological aspects of the material used in 3D printing processes for selected medical applications. Materials. 2020; 13(23): 5437. PubMed Abstract | Publisher Full Text | Free Full Text

[182] 182. Ng WL, Chan A, Ong YS, et al.: Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual and Physical Prototyping. 2020; 15(3): 340–358. Publisher Full Text

[183] 183. Liu Y, Shang X, Shen Z, et al.: 3D Deep Learning for 3D Printing of Tooth Model. IEEE. 2019; 274–279.

[184] 184. Ariji Y, Fukuda M, Kise Y, et al.: Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. 2019; 127(5): 458–463. PubMed Abstract | Publisher Full Text

[185] 185. Knoops PG, Papaioannou A, Borghi A, et al.: A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci. Rep. 2019; 9(1): 1–12.

[186] 186. Zanjani FG, Moin DA, Verheij B, et al.: Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. PMLR. 2019; 557–571. Publisher Full Text

[187] 187. Kim T, Cho Y, Kim D, et al.: Tooth segmentation of 3D scan data using generative adversarial networks. Appl. Sci. 2020; 10(2): 490. Publisher Full Text

[188] 188. Lian C, Wang L, Wu T-H, et al.: Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners. IEEE Trans. Med. Imaging. 2020; 39(7): 2440–2450. PubMed Abstract | Publisher Full Text

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Abstract

Keywords

Introduction

Artificial Intelligence (AI) for Healthcare

Figure 1. Artificial Intelligence types.

Figure 2. Key aspects of artificial intelligence.

Application of AI in Dentistry

Figure 3. Summary of the hierarchy and major dental applications of AI.

Figure 4. The categories of AI applications in dentistry.

State-of-the-art AI technology in Dentistry

ANNs

Augmented Reality and Virtual Reality

Data Mining

Applications in the Various Fields of Dentistry

Figure 5. Dental Specialties based on Canadian Dental Association.

Figure 6. The comprehensive dental care system of the future with AI.

Dental Public Health

Endodontics

Oral and Maxillofacial Surgery

Orthodontics and Dentofacial Orthopedics

Periodontics

Oral and Maxillofacial Radiology

Oral Medicine and Pathology

Pediatric Dentistry

Prosthodontics

Table 1. Summary of relevant studies on artificial intelligence applications in dentistry.

AI Applications in 3D Digital Dentistry

Figure 7. The process of digitalizing the dental system.

Strengths and Weaknesses of AI in Dentistry

Strengths of AI in Dentistry

Weaknesses of AI in Dentistry

Figure 8. Strengths and weaknesses of AI.

The Main Challenges of using AI in Dentistry

Conclusion

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Browse by related subjects

Competing Interests Policy

Stay Updated