Enhancing TMJ Disorder Diagnosis via AI-Assisted Segmentation

Temporomandibular Joint Disorder (TMD) is an umbrella term for diseases that affect the jaw. It is a common but often underdiagnosed condition that can cause significant jaw pain, limited movement, and long-term oral health issues. Accurate diagnosis relies heavily on the precise identification of tiny jaw structures in medical imaging, a task that remains challenging due to anatomical complexity and variability.

This project builds on our previous research and aims to advance the diagnosis of TMD using AI-assisted imaging. By combining a large and diverse dataset from radiology centers across Edmonton, with recent deep learning architectures, we aim to develop and AI model that is accurate, clinically relevant and useful in the identification of TMD.

We aim to create a diagnostic tool that can seamlessly integrate into clinical workflows, helping oral health professionals diagnose TMJ disorders with greater confidence and efficiency. This work represents a significant step toward modernizing TMD diagnostics, reducing diagnostic delays, and ultimately improving patient care through the power of artificial intelligence.

Faculty Supervisor:

Fabiana Marques

Student:

Partner:

Federal University of Parana

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

University of Alberta

Program: