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Can Deep Learning Methods Differentiate Temporomandibular Joint Disorders From Healthy Joints? A 3D Artificial

İbrahim Şevki Bayrakdar^1,2, Sevda Kurt-Bayrakdar^2,3, Alican Kuran⁴

¹Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey.

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Summary

This summary is machine-generated.

Deep learning models accurately segment mandibular condyles and differentiate temporomandibular joint disorders (TMD) on CBCT scans. Further research is needed for precise TMD grading.

Keywords:

artificial intelligence cone beam computed tomography deep learning temporomandibular disorders temporomandibular joint

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Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Oral and Maxillofacial Surgery

Background:

Interpreting bone-related temporomandibular joint disorders (TMD) on CBCT scans is challenging.
Advanced AI models are crucial for accurate TMD diagnosis and subcategorization.

Purpose of the Study:

Develop deep learning models for comprehensive temporomandibular joint evaluation on CBCT scans.
Automate segmentation, classification, and grading of TMD on CBCT images.

Main Methods:

nnU-Net v2 for mandibular condyle segmentation.
3D CNN for classifying healthy vs. pathological condyles and differentiating five TMD types.
Exploratory nnU-Net v2 for grading erosion, osteophyte formation, and sclerosis severity.

Main Results:

Segmentation model achieved 0.87 DSC and 0.77 IoU.
Classification F1-scores ranged from 0.65 (healthy vs. TMD) to 0.88 (flattening, sclerosis).
Highest performance in grading was observed for Grade 1 erosion, Grade 2 osteophyte formation, and Grade 3 sclerosis.

Conclusions:

Deep learning shows clinical relevance in condyle segmentation and TMD differentiation.
Larger, balanced datasets are required to enhance TMD grading accuracy.