Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Area of Science:

• Dentistry
• Artificial Intelligence
• Biomedical Engineering

Background:

• Clinical translation of AI-driven predictive modeling in dentistry lags behind research advancements.
• Predictive modeling and deep learning analyze complex relationships in occlusion and TMJ function.
• AI complements imaging modalities like CBCT and MRI for TMJ analysis.

Purpose of the Study:

• To explore the application of predictive modeling and deep learning in dentistry.
• To identify clinical trends and associations related to occlusion and TMJ function.
• To understand the role of AI in managing temporomandibular disorders (TMDs).

Main Methods:

• Narrative review of AI applications in dental research.
• Analysis of predictive modeling techniques for TMJ dynamics.
• Exploration of deep learning for quantifying occlusal and TMJ relationships.

Main Results:

• Noninvasive chairside tools generate data for predicting jaw movement and TMJ/occlusion variations.
• Predictive modeling aids in understanding individualized TMJ function norms.
• Challenges remain in managing occlusal factors and mitigating bias in TMJ analysis.

Conclusions:

• AI and predictive modeling offer valuable insights into complex dental conditions like TMDs.
• Understanding TMJ function and occlusion is crucial for effective TMD management.
• Noninvasive techniques utilizing AI show promise for improving diagnosis and treatment outcomes.