Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases

Aathira Surendran; Sunita Shrivastav; Gaurav Srivastav

doi:10.12688/f1000research.133328.2

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Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases

[version 2; peer review: 1 approved]

Aathira Surendran ¹, Sunita Shrivastav¹, Gaurav Srivastav²

PUBLISHED 15 Jul 2024

Author details Author details

¹ Department of Orthodontics, Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, 442001, India
² Department of Artificial Intelligence and Data Science, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, 442001, India

Aathira Surendran
Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Sunita Shrivastav
Roles: Supervision, Validation, Writing – Review & Editing

Gaurav Srivastav
Roles: Software, Supervision, Writing – Review & Editing

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This article is included in the Datta Meghe Institute of Higher Education and Research collection.

Abstract

Introduction: Temporomandibular disorder (TMD) encompasses several clinical manifestations, which are characterized by temporomandibular joint and masticatory muscle discomfort and dysfunction (TMJ). The best imaging technique for evaluating TMJ is magnetic resonance imaging (MRI), which makes it possible to see the anatomical and pathological characteristics of every joint component. In recent years, convolutional neural networks -based deep learning algorithms have been favoured because of their outstanding capability in recognizing objects in medical images. The objective of this study is to assess, compare and co-rrelate articular disc morphology by automated segmentation using deep learning on MRI images in skeletal Class II (vertical growth pattern) TMD cases as compared to skeletal Class II (horizontal growth pattern) TMD cases and Class I non-TMD cases

Methods: Grading of skeletal Class II (vertical growth pattern) cases and skeletal Class II (horizontal growth pattern) cases based on severity of TMD will be carried out using diagnostic criteria for temporomandibular disorders. Bilateral sagittal as well as coronal MRI images will be obtained. A convolutional neural network (CNN) encoder-decoder named U-Net will be used to segment the articular disc on MRI. Understanding the nature of variations between Class I and both types of Class IIs will help orthodontists to better predict the potential risk for the development of TMDs and accordingly take precautions while doing treatment in such cases. Moreover, it can be used to automate TMD diagnosis and other smart applications.

Conclusions: This study will aid in identifying articular disc morphology on MRI. The deep learning algorithms with effective data augmentation may perform better in MRI readings than human clinicians when using the same data, which will be advantageous for TMD diagnosis.

Keywords

temporomandibular disorder, TMD, articular disc, Class II TMD cases, automated segmentation, deep neural network, convolutional neural network

Corresponding author: Aathira Surendran

Competing interests: No competing interests were disclosed.

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

Copyright: © 2024 Surendran A 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: Surendran A, Shrivastav S and Srivastav G. Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases [version 2; peer review: 1 approved]. F1000Research 2024, 12:855 (https://doi.org/10.12688/f1000research.133328.2) First published: 20 Jul 2023, 12:855 (https://doi.org/10.12688/f1000research.133328.1) Latest published: 15 Jul 2024, 12:855 (https://doi.org/10.12688/f1000research.133328.2)

Revised Amendments from Version 1

We have done the necessary changes in this new version of the manuscript like improved the introduction part by classifying TMD and the overlapping symptoms and also the recent literature on conservative approaches for the treatment of TMD.

See the authors' detailed response to the review by Martina Ferrillo

Introduction

Background and rationale

Temporomandibular disorder (TMD) encompasses several clinical manifestations which is characterized by temporomandibular joint (TMJ) and masticatory muscle discomfort and dysfunction.¹ TMD is frequently characterized by facial and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ.²

TMD patients may present overlapping symptoms with other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

The expected incidence rate of TMD at first onset is 3.9%, with mild to severe pain and impairment, based on an American prospective cohort study of the general public. In industrialized nations, it affects 5–12% of the population.³ As reported previously in the literature, TMD is more common in cases with skeletal Class II (vertical growth pattern) followed by skeletal Class II (horizontal growth pattern) as compared to Class I cases. This was first reported by Pancherz in 1999.⁴

TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.⁵

Non-invasive treatments, including behavioral therapy, physical therapy, drugs, occlusal splints, and laser therapy, are commonly recommended as the initial approach to improve joint range of motion, reduce pain, and prevent further degeneration. These methods are considered first-line treatments for various joint conditions. In contrast, minimally invasive techniques, such as intra-articular (IA) injections of sodium hyaluronate (including hyaluronic acid or HA), have gained attention for their potential effectiveness. IA injections, particularly when combined with joint arthrocentesis and lavage, are being explored as a viable treatment option.⁶

Laser acupuncture therapy (LAT) has been proposed as a needle-free alternative to traditional acupuncture, utilizing low-intensity laser light to stimulate traditional acupuncture points. This approach is characterized by its simplicity, lack of invasiveness, painlessness, and inherent safety compared to needle acupuncture therapy.⁷

The best imaging technique for evaluating TMJ is magnetic resonance imaging (MRI), which makes it possible to see both the anatomical and pathological characteristics of every joint component. The shape and location of the articular disc, the mandibular condyle’s shape and surface characteristics, atypical bone marrow signal in the mandible and temporal bone can all be assessed using an MRI.⁵ Disc displacement or deformation, an intracapsular condition affecting the disc-condylar complex, is one of the most prominent subgroups in individuals with TMD who have articular abnormality, with an incidence of 30–60%. An MRI is necessary for assessing variations in articular disc morphology and to predict the treatment outcome.³

Artificial intelligence (AI)-based dental applications have been researched to simplify dental care and enhance the health of more cases at a cheap cost and are attracting interest in a variety of clinical fields. By implementing AI-based dental applications, dental professionals can reduce the amount of time they spend performing regular tasks, which will enable them to provide more individualized, preventive, and collaborative dental care. In recent years, convolutional neural networks (CNN)-based deep learning algorithms have been more favoured because of their outstanding capabilities to recognize objects in medical images. Furthermore, as computational power has increased and open-source frameworks have become more common, CNN development has been dramatically facilitated. As a result, for detection and segmentation purposes, deep learning has been extensively used, showing encouraging results. There have been reports of a number of deep neural network topologies, including the CNN-derived fully convolutional network’s U-Net and SegNet variants.⁸

Clinicians and radiologists will be able to save time and interpret pictures more accurately if they can correctly identify the TMJ area’s major structures on MRI imaging. It can also be the basis for a lot of clever applications, including automatically diagnosing TMDs. It is without doubt important to accomplish this goal to automatically segregate TMJ structures from MRI images. Using these deep learning techniques, the mandibular condyle, articular eminence, and TMJ articular disc all are automatically detected. The TMJ segmentation of anatomical features in MRI volumes is made innovative in the current diagnostic image analysis investigation employing CNN-based DL methods.

Objectives

1. To evaluate articular disc morphology in Class I (Non-TMD) cases, skeletal Class II (vertical growth pattern) TMD cases, skeletal Class II (horizontal growth pattern) TMD cases.
2. To compare articular disc morphology in skeletal Class II (vertical growth pattern) TMD cases with skeletal Class II (horizontal growth pattern) TMD cases and Class I (Non-TMD) cases.
3. To correlate the variation in articular disc morphology with skeletal pattern.

Protocol

Study design and setting

An observational and analytical study will be conducted at Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research (DMIHER), Sawangi, Wardha, Maharashtra in collaboration with the Faculty of Engineering and Technology (FEAT), DMIHER and the Department of Radiology, Acharya Vinobha Bhave Rural Hospital (AVBRH), Sawangi, Wardha.

A total of 90 adult cases (Class I, Class II (vertical and horizontal growth pattern)) will be chosen from the Sharad Pawar Dental College’s outpatient department (OPD) of orthodontics and dentofacial orthopaedics in Sawangi, Wardha.

Participants

Inclusion criteria

• Class I malocclusion cases.
• Skeletal Class II (vertical growth pattern) and TMD cases.
• Cases with skeletal Class II (horizontal growth pattern) and TMD.
• Cases with permanent dentition.
• Older than 18 years of age.
• The cases will be classified into Class I and Class II (vertical and horizontal) by cephalometric measurements as shown in the Table 1.

Table 1. Cephalometric parameters for case selection of Class I and Class II (vertical and horizontal growth).

Cephalometric measurements	Class I	Class II (vertical)	Class II (horizontal)
ANB angle	2°	>2°	>2°
Wits appraisal	0–1 mm	<2 mm	<2 mm
Frankfort mandibular plane angle	22–28°	>30°	<20°
Beta angle	27–33°	<25°	<25°
Mandibular plane angle	17–28°	>28°	<28°

Exclusion criteria

• Skeletal Class II (vertical growth pattern) non-TMD cases.
• Non-TMD Class II (horizontal growth pattern) cases.
• Class III cases.
• Cases with myofascial pain dysfunction (MPD) and myalgia.
• Prior treatment involving temporomandibular joint surgery.
• Cases with any skeletal disorders such as cherubism, osteoporosis.

Methods

Class II and Class I patients will be assessed using both ANB and Wits appraisal. The patient will then be diagnosed according to the diagnostic criteria for temporomandibular disorders (DC/TMD).⁹ Each participant will be asked for their written, informed consent. Based on the severity of TMD, skeletal Class II (vertical and horizontal growth pattern) cases will be graded using DC/TMD criteria. Both bilateral saggital and coronal MRI images will be obtained.

The articular disc of the TMJ on MRI will be identified and manually segmented with the help of technical expertise from the Faculty of Artificial Intelligence and Machine Learning (AIML) at FEAT College, Datta Meghe Institute of Higher Education and Research. After segmentation those images will be used as the dataset. The dataset showing the normal position of articular discs will be randomly split into a training data set and test set. To reduce the overfitting of the network, the dropout layer is placed behind the convolutional layers and max-pooling layers. Region of interests around the articular disc will be extracted from the datasets.

Region of interests will be automatically cropped from the images using Python 3.11 algorithms. Convolutional neural network (CNN) encoder-decoder named U-Net model architecture using the software Visual Studio Code with the Python programming language will be used to segment the articular disc on MRI. The variation in articular disc morphology will be evaluated and compared in Class II (vertical and horizontal growth pattern) with Class I Non-TMD cases using deep learning algorithms on MRI.

Outcomes

Primary outcome: To evaluate articular disc morphology in skeletal class II vertical and horizontal growth pattern.

Secondary outcome: To compare and correlate the variation in articular disc morphology with skeletal pattern.

Tertiary outcome: For doctors and radiologists, the ability to precisely recognise important features in MRI of the TMJ region will save time and increase accuracy. Also understanding the nature of variations between Class I and both types of Class IIs will help orthodontists to better predict the potential risk for development of TMDs and accordingly take precautions while carrying out treatment in such cases. Moreover, it can be used to automate TMD diagnosis and other smart applications. This is why automated segmentation of TMJ structures with MRI is clearly necessary.

Bias

Bias will be minimized by random selection of patients based on the inclusion and exclusion criteria.

Study sample

The calculation of sample size was carried out as follows:

Formula using mean difference

n 1 = n 2 = 2 \frac{{(Z_{α} + Z_{β})}^{2} σ^{2}}{{(δ)}^{2}}

Z_α = 1.64

α = Type I error at 5% at both sides two tailed

Ζ_β = 0.84 (1 - β) = power at 80%

σ = standard deviation

Primary variable: Articular disc variation (mm)

Class I TMD group = 1.38 ± 0.20 (John et al. (2020)¹⁰)

Class II (horizontal growth) TMD (mm)) group = 1.51 ± 0.20 (John et al. (2020)¹⁰)

Clinically relevant difference = 0.13

Pooled standard deviation = (0.20+0.20)/2 = 0.2

N1 = 2^{*} [{(1.64 + 0.84)}^{2} {(0.2)}^{2}] / {(0.13)}^{2} = 30

Total samples required = 30 per group.

Total sample size = 90

The sample would be divided into three groups based on the inclusion and exclusion criteria:

Group A (control group): 30 Class I (Non-TMD) cases.

Group B: 30 skeletal Class II (vertical growth pattern) cases.

Group C: 30 skeletal Class II (horizontal growth pattern) cases.

Statistical analyses

All the demographic outcome data will be presented using descriptive statistics for categorial variables in terms of frequency and percentage for continuous variables in terms of mean, standard deviation and median. Results will be analysed using SPSS version 27 (RRID:SCR_019096) for statistical analysis. Outcome variables will be tested for normality using the Kolmogorov-Smirnov test for continuous data.

All the samples will be distributed amongst the category of Class I non-TMD, Class II (vertical growth pattern) TMD cases and Class II (horizontal growth pattern) TMD cases as per the cephalometric measurements. Articular disc variation at different positions will be evaluated between these three groups using ANOVA or Kruskal Wallis test.

ANOVA or a Kruskal Wallis test will be used to find the result amongst the three groups for the outcome variable for the significance in mean difference. If the data are normally distributed an ANOVA test will be used and if the data are not normally distributed a non-parametric test (Kruskal Wallis) will be used to find the significant difference.

Dissemination

The original research will be published in a research article of an index journal. To ensure the original study receives the most exposure possible, the trial results will be released as open access.

Study status

The deep learning model is currently being prepared and the study will commence in May 2023.

Discussion

Nowadays, a lot of people have TMD due to the increased stress brought on by our fast-paced society. TMD is regarded as a collection of orofacial joint and muscle problems marked by pain, abnormal joint noises, and uneven jaw function. Although only 30% of subjects may be aware of such symptoms, the majority of research show that at least 50% of people have at least one symptom (such as muscle tenderness or joint clicking).¹¹

There is variation in the morphology of the hard and soft tissue structures of TMJ specially of the articular disc. At times visualization and clarity of joint morphology and the articular disc may hinder the accuracy of diagnosis. What is the extent of variation and what type of disc morphology may cause a higher potential risk of TMD needs to be explored for early and more accurate prediction.

Hirata et al. (2007) studied the articular disc shape and location in patients with disc displacement, as well as the shape of the temporomandibular joint’s articular eminence. In the study, 14 individuals with bilateral disc displacement and no unilateral reduction were included. For evaluation, they employed magnetic resonance scans and showed that the chance of non-reducing disc displacement may be influenced by the shape of the articular disc and eminence.¹²

In 2020, John et al., observed that a Class II condition with a vertical growth pattern had the highest probability of developing a TMD with internal disk derangements and reduced anterior and posterior joint spaces compared to Class II with a horizontal growth pattern or Class I condition.¹⁰

Ito et al. (2022), conducted a study that assessed fully segmentation of the temporomandibular joint’s articular disc by automated means. Ten patients with anterior disc displacement and ten healthy control participants with normal articular discs were enrolled in the study. On MRI, they applied a semantic segmentation method based on deep learning. The study showed that this method for segmenting articular discs using automated deep learning on MRI generated encouraging preliminary results suggesting that the approach might be employed in clinical practice for the evaluation of temporomandibular disorders.³

Based on the findings of these previous studies, this planned study would enable us to recognize changes in articular disc shape, changes in articular disc location, and morphological abnormalities that can be evaluated by the deep learning network. The outcome of the treatment and diagnostic efficiency for TMD’s may improve using this technique.

Ethical considerations

Ethical approval has been granted by the Institutional Review Board of Datta Meghe Institute of Higher Education and Research, Sawangi, Wardha. (Reference number: DMIHER (DU)/IEC/2023/570 on 06/02/2023).

Data availability

Underlying data

No data are associated with this article.

Reporting guidelines

Zenodo: STROBE checklist for ‘Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) temporomandibular joint (TMD) cases, Class II (horizontal) TMD cases and Class I non-TMD cases’, https://www.doi.org/10.5281/zenodo.7853465.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0)

References

1. dos Santos EA , Peinado BRR, Frazão DR, et al.: Association between temporomandibular disorders and anxiety: A systematic review. Front. Psychiatry. 2022 Oct 13; 13: 990430. Publisher Full Text
2. Scrivani SJ, Keith DA, Kaban LB: Temporomandibular Disorders. N. Engl. J. Med. 2008 Dec 18; 359(25): 2693–2705. Publisher Full Text
3. Ito S, Mine Y, Yoshimi Y, et al.: Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci. Rep. 2022; 12(1): 221. PubMed Abstract | Publisher Full Text | Free Full Text
4. Ruf S, Pancherz H: Temporomandibular joint remodeling in adolescents and young adults during Herbst treatment: A prospective longitudinal magnetic resonance imaging and cephalometric radiographic investigation. Am. J. Orthod. Dentofac. Orthop. Off. Publ. Am. Assoc. Orthod. Its Const. Soc. Am. Board Orthod. 1999 Jun; 115(6): 607–618. Publisher Full Text
5. Aiken A, Bouloux G, Hudgins P: MR Imaging of the Temporomandibular Joint. Magn. Reson. Imaging Clin. N. Am. 2012 Aug; 20(3): 397–412. Publisher Full Text
6. Agostini F, Ferrillo M, Bernetti A, et al.: Hyaluronic acid injections for pain relief and functional improvement in patients with temporomandibular disorders: An umbrella review of systematic reviews. J. Oral Rehabil. 2023; 50(12): 1518–1534. PubMed Abstract | Publisher Full Text
7. Madani A, Ahrari F, Fallahrastegar A, et al.: A randomized clinical trial comparing the efficacy of low-level laser therapy (LLLT) and laser acupuncture therapy (LAT) in patients with temporomandibular disorders. Lasers Med. Sci. 2020 Feb; 35(1): 181–192. PubMed Abstract | Publisher Full Text
8. Li M, Punithakumar K, Major PW, et al.: Temporomandibular joint segmentation in MRI images using deep learning. J. Dent. 2022; 127: 104345. PubMed Abstract | Publisher Full Text
9. Schiffman E, Ohrbach R, Truelove E, et al.: Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†. J. Oral Facial Pain Headache. 2014; 28(1): 6–27. PubMed Abstract | Publisher Full Text | Free Full Text
10. John ZAS, Shrivastav SS, Kamble R, et al.: Three-dimensional comparative evaluation of articular disc position and other temporomandibular joint morphology in Class II horizontal and vertical cases with Class I malocclusion. Angle Orthod. 2020 Sep; 90(5): 707–714. PubMed Abstract | Publisher Full Text | Free Full Text
11. Shen S, Ye M, Wu M, et al.: MRI and DC/TMD methods analyze the diagnostic accuracy of the change in articular disc of temporomandibular joint. Comput. Math. Methods Med. 2022; 2022: 1–7. PubMed Abstract | Publisher Full Text | Free Full Text
12. Hirata FH, Guimarães AS, de Oliveira JX , et al.: Evaluation of TMJ articular eminence morphology and disc patterns in patients with disc displacement in MRI. Braz. Oral Res. 2007 Sep; 21(3): 265–271. PubMed Abstract | Publisher Full Text

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Version 2

VERSION 2 PUBLISHED 20 Jul 2023

Author details Author details

Sunita Shrivastav
Roles: Supervision, Validation, Writing – Review & Editing

Gaurav Srivastav
Roles: Software, Supervision, 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 (2)

version 2

Revised

Published: 15 Jul 2024, 12:855

https://doi.org/10.12688/f1000research.133328.2

version 1

Published: 20 Jul 2023, 12:855

https://doi.org/10.12688/f1000research.133328.1

© 2024 Surendran A 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.

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Surendran A, Shrivastav S and Srivastav G. Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases [version 2; peer review: 1 approved]. F1000Research 2024, 12:855 (https://doi.org/10.12688/f1000research.133328.2)

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

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Open Peer Review

Version 2

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PUBLISHED 15 Jul 2024

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Reviewer Report 22 Jul 2024

Martina Ferrillo, University of Catanzaro, Catanzaro, Italy

Approved

https://doi.org/10.5256/f1000research.168904.r303157

The authors modified the paper according to my suggestions. ... Continue reading

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PUBLISHED 20 Jul 2023

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Reviewer Report 12 Feb 2024

Martina Ferrillo, University of Catanzaro, Catanzaro, Italy

Approved with Reservations

https://doi.org/10.5256/f1000research.146307.r232583

Temporomandibular disorders (TMD) are a collective of various symptoms caused by abnormalities in the temporomandibular joint (TMJ) and related structures. It affects 20% to 40% of the general population. Lifestyle habits, like alcohol consumption and smoking, are considered to be linked to TMD. This study aimed to assess, compare and co-relate articular disc morphology by automated segmentation using deep learning on MRI images in skeletal Class II (vertical growth pattern) TMD cases as compared to skeletal Class II (horizontal growth pattern) TMD cases and Class I non-TMD cases.

The study is of scientific interest and in line with the aims of the Journal. However, there are some issues that should be added.

I suggest improving the abstract section:

“temporomandibular joint and masticatory muscle discomfort and dysfunction (TMJ).” Please move “(TMJ)” after “temporomandibular joint”.

I suggest improving the introduction section on TMD:

Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).
“and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).
Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Methods:

“A total of 90 adult cases” - Please move this information in the Result Section.
Use in all the text and tables “P” or “p-value”.
Class II patients were assessed using ANB or Wits? Or Both? Please specify.
The same question for Class I.
Add reference for DC/TMD.

Results are well described.
Discussion should be improved reposting more recent literature on this topic.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Yes

References

1. Schiffman E, Ohrbach R, Truelove E, Look J, et al.: Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†.J Oral Facial Pain Headache. 2014; 28 (1): 6-27 PubMed Abstract | Publisher Full Text
2. Agostini F, Ferrillo M, Bernetti A, Finamore N, et al.: Hyaluronic acid injections for pain relief and functional improvement in patients with temporomandibular disorders: An umbrella review of systematic reviews.J Oral Rehabil. 2023; 50 (12): 1518-1534 PubMed Abstract | Publisher Full Text
3. Madani A, Ahrari F, Fallahrastegar A, Daghestani N: A randomized clinical trial comparing the efficacy of low-level laser therapy (LLLT) and laser acupuncture therapy (LAT) in patients with temporomandibular disorders.Lasers Med Sci. 2020; 35 (1): 181-192 PubMed Abstract | Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Temporomandibular disorders

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.

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Author Response 15 Jul 2024

Aathira Surendran, Department of Orthodontics, Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research, Wardha, 442001, India

15 Jul 2024

Author Response
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD ... Continue reading
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).

Response- i have included this in the introduction section.

2. “and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

Response- i have reported it.

3. Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Response- I have added this literature in the introduction

4. Methods:

“A total of 90 adult cases” - Please move this information in the Result Section

Use in all the text and tables “P” or “p-value”.

Class II patients were assessed using ANB or Wits? Or Both? Please specify.

The same question for Class I.

Add reference for DC/TMD

Response- as this is a study protocol there isn't a result section so I couldn't move the information of total cases.
I have made the corrections like specified the method used for assessing Class II and Class I patients and have added the reference for DC/TMD.
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).

Response- i have included this in the introduction section.

2. “and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

Response- i have reported it.

3. Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Response- I have added this literature in the introduction

4. Methods:

“A total of 90 adult cases” - Please move this information in the Result Section

Use in all the text and tables “P” or “p-value”.

Class II patients were assessed using ANB or Wits? Or Both? Please specify.

The same question for Class I.

Add reference for DC/TMD

Response- as this is a study protocol there isn't a result section so I couldn't move the information of total cases.
I have made the corrections like specified the method used for assessing Class II and Class I patients and have added the reference for DC/TMD.
Competing Interests: No competing interests were disclosed. Close
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Author Response 15 Jul 2024

Aathira Surendran, Department of Orthodontics, Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research, Wardha, 442001, India

15 Jul 2024

Author Response
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD ... Continue reading
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).

Response- i have included this in the introduction section.

2. “and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

Response- i have reported it.

3. Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Response- I have added this literature in the introduction

4. Methods:

“A total of 90 adult cases” - Please move this information in the Result Section

Use in all the text and tables “P” or “p-value”.

Class II patients were assessed using ANB or Wits? Or Both? Please specify.

The same question for Class I.

Add reference for DC/TMD

Response- as this is a study protocol there isn't a result section so I couldn't move the information of total cases.
I have made the corrections like specified the method used for assessing Class II and Class I patients and have added the reference for DC/TMD.
Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).

Response- i have included this in the introduction section.

2. “and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

Response- i have reported it.

3. Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Response- I have added this literature in the introduction

4. Methods:

“A total of 90 adult cases” - Please move this information in the Result Section

Use in all the text and tables “P” or “p-value”.

Class II patients were assessed using ANB or Wits? Or Both? Please specify.

The same question for Class I.

Add reference for DC/TMD

Response- as this is a study protocol there isn't a result section so I couldn't move the information of total cases.
I have made the corrections like specified the method used for assessing Class II and Class I patients and have added the reference for DC/TMD.
Competing Interests: No competing interests were disclosed. Close
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VERSION 2 PUBLISHED 20 Jul 2023

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Version 2 (revision) 15 Jul 24	read
Version 1 20 Jul 23	read

Martina Ferrillo, University of Catanzaro, Catanzaro, Italy

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22 Jul 2024 | for Version 2

Martina Ferrillo, University of Catanzaro, Catanzaro, Italy

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Approved

The authors modified the paper according to my suggestions. In my opinion, the paper is suitable for indexing.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Temporomandibular disorders

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.

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23 Views

12 Feb 2024 | for Version 1

Martina Ferrillo, University of Catanzaro, Catanzaro, Italy

23 Views Cite this report Responses(1)

Approved With Reservations

“temporomandibular joint and masticatory muscle discomfort and dysfunction (TMJ).” Please move “(TMJ)” after “temporomandibular joint”.

I suggest improving the introduction section on TMD:

Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).
“and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).
Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Methods:

“A total of 90 adult cases” - Please move this information in the Result Section.
Use in all the text and tables “P” or “p-value”.
Class II patients were assessed using ANB or Wits? Or Both? Please specify.
The same question for Class I.
Add reference for DC/TMD.

Results are well described.
Discussion should be improved reposting more recent literature on this topic.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Partly
Are the datasets clearly presented in a useable and accessible format?

Yes

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Temporomandibular disorders

Respond to this report

Responses (1)

Author Response

15 Jul 2024

Aathira Surendran, Department of Orthodontics, Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research, Wardha, 442001, India

Thank you ma'am for your thorough analysis of my article. I have made the following changes based on your review.

1. Classification. Please refer to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Thus, report that TMD could be divided in Group I: muscle disorders (including myofascial pain with and without mouth opening limitation); Group II: including disc displacement with or without reduction and mouth opening limitation; Group III: arthralgia, arthritis, and arthrosis.). (cite and refer to: Schiffman et al., 2014¹).

Response- i have included this in the introduction section.

2. “and pre-auricular regional pain, malocclusion, limited jaw movement, and clicking and locking of the TMJ “. Please report that TMD patients may present overlapping symptoms whit other chronic pain conditions, including headache, fibromylagia, and neurological conditions, probably through the phenomenon of central sensitization (mainly allodynia and hyperalgesia).

Response- i have reported it.

3. Please report more recent literature on conservative approaches and report new proposed treatment in the scientific literature (Agostini et al., 2023²; Madani et al., 2020³).

Response- I have added this literature in the introduction

4. Methods:

“A total of 90 adult cases” - Please move this information in the Result Section
Use in all the text and tables “P” or “p-value”.
Class II patients were assessed using ANB or Wits? Or Both? Please specify.
The same question for Class I.
Add reference for DC/TMD

Response- as this is a study protocol there isn't a result section so I couldn't move the information of total cases.
I have made the corrections like specified the method used for assessing Class II and Class I patients and have added the reference for DC/TMD.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the 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 approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. dos Santos EA , Peinado BRR, Frazão DR, et al.: Association between temporomandibular disorders and anxiety: A systematic review. Front. Psychiatry. 2022 Oct 13; 13: 990430. Publisher Full Text

[2] 2. Scrivani SJ, Keith DA, Kaban LB: Temporomandibular Disorders. N. Engl. J. Med. 2008 Dec 18; 359(25): 2693–2705. Publisher Full Text

[3] 3. Ito S, Mine Y, Yoshimi Y, et al.: Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci. Rep. 2022; 12(1): 221. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Ruf S, Pancherz H: Temporomandibular joint remodeling in adolescents and young adults during Herbst treatment: A prospective longitudinal magnetic resonance imaging and cephalometric radiographic investigation. Am. J. Orthod. Dentofac. Orthop. Off. Publ. Am. Assoc. Orthod. Its Const. Soc. Am. Board Orthod. 1999 Jun; 115(6): 607–618. Publisher Full Text

[5] 5. Aiken A, Bouloux G, Hudgins P: MR Imaging of the Temporomandibular Joint. Magn. Reson. Imaging Clin. N. Am. 2012 Aug; 20(3): 397–412. Publisher Full Text

[6] 6. Agostini F, Ferrillo M, Bernetti A, et al.: Hyaluronic acid injections for pain relief and functional improvement in patients with temporomandibular disorders: An umbrella review of systematic reviews. J. Oral Rehabil. 2023; 50(12): 1518–1534. PubMed Abstract | Publisher Full Text

[7] 7. Madani A, Ahrari F, Fallahrastegar A, et al.: A randomized clinical trial comparing the efficacy of low-level laser therapy (LLLT) and laser acupuncture therapy (LAT) in patients with temporomandibular disorders. Lasers Med. Sci. 2020 Feb; 35(1): 181–192. PubMed Abstract | Publisher Full Text

[8] 8. Li M, Punithakumar K, Major PW, et al.: Temporomandibular joint segmentation in MRI images using deep learning. J. Dent. 2022; 127: 104345. PubMed Abstract | Publisher Full Text

[9] 9. Schiffman E, Ohrbach R, Truelove E, et al.: Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†. J. Oral Facial Pain Headache. 2014; 28(1): 6–27. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. John ZAS, Shrivastav SS, Kamble R, et al.: Three-dimensional comparative evaluation of articular disc position and other temporomandibular joint morphology in Class II horizontal and vertical cases with Class I malocclusion. Angle Orthod. 2020 Sep; 90(5): 707–714. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. Shen S, Ye M, Wu M, et al.: MRI and DC/TMD methods analyze the diagnostic accuracy of the change in articular disc of temporomandibular joint. Comput. Math. Methods Med. 2022; 2022: 1–7. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Hirata FH, Guimarães AS, de Oliveira JX , et al.: Evaluation of TMJ articular eminence morphology and disc patterns in patients with disc displacement in MRI. Braz. Oral Res. 2007 Sep; 21(3): 265–271. PubMed Abstract | Publisher Full Text

Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Background and rationale

Objectives

Protocol

Study design and setting

Participants

Table 1. Cephalometric parameters for case selection of Class I and Class II (vertical and horizontal growth).

Methods

Outcomes

Bias

Study sample

Statistical analyses

Dissemination

Study status

Discussion

Ethical considerations

Data availability

Underlying data

Reporting guidelines

References

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