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

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

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. ⁶

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.

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.

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.

Inclusion criteria •

Class I malocclusion cases.

Exclusion criteria •

Skeletal Class II (vertical growth pattern) non-TMD cases.

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

The calculation of sample size was carried out as follows:

Formula using mean difference n 1 = n 2 = 2 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) ⁷ )

Class II (horizontal growth) TMD (mm)) group = 1.51 ± 0.20 (John et al. (2020) ⁷ )

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.

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.

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.

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

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).