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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Multi-class segmentation of temporomandibular joint using ensemble deep learning.

Kyubaek Yoon¹, Jae-Young Kim², Sun-Jong Kim³

¹Department of Artificial Intelligence and Software, Ewha Womans University, Seoul, South Korea.

Scientific Reports

|August 19, 2024

Summary

This summary is machine-generated.

Deep learning models accurately segment temporomandibular joint (TMJ) components in MRI scans. This AI tool improves diagnosis of TMJ disorders like disc displacement and osteoarthritis.

Keywords:

Artificial intelligence Deep learning Magnetic resonance imaging Segmentation Temporomandibular joint

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Temporomandibular joint (TMJ) disorders cause significant orofacial discomfort.
Accurate diagnosis relies on magnetic resonance imaging (MRI) analysis of TMJ components.
Image quality variations and complex anatomy challenge precise TMJ diagnosis.

Purpose of the Study:

To develop and validate deep learning models for automated segmentation of TMJ components (temporal bone, disc, condyle).
To assess the efficacy of AI-augmented tools in enhancing diagnostic accuracy for TMJ disorders.

Main Methods:

Developed deep learning models for automatic segmentation of temporal bone, disc, and condyle in MRI.
Trained and validated models on a dataset of 3693 MRI scans from 542 patients.
Evaluated an ensemble model combining five individual models.

Main Results:

Internal testing: Ensemble model achieved Dice similarity coefficients of 0.867 (temporal bone), 0.733 (disc), 0.904 (condyle).
External validation: Achieved Dice coefficients of 0.720 (temporal bone), 0.604 (disc), 0.800 (condyle).
AI tools improved physician diagnostic accuracy, particularly for differentiating anterior disc displacement and osteoarthritis.

Conclusions:

Automated TMJ segmentation using deep learning is a promising approach.
AI-driven segmentation aids in refining the diagnosis and treatment of TMJ disorders.
Enhanced diagnostic capabilities can lead to improved patient management for TMJ conditions.