Temporomandibular joint assessment in MRI images using artificial intelligence tools: Where are we now? A systematic review.

Abstract

Objectives: To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.

Methods: Studies were gathered by searching five electronic databases and partial grey literature up to May 27th, 2024. Studies in humans using AI algorithms to detect or to diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.

Results: Thirteen studies were included in this systematic review. Most of studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.

Conclusions: The integration of AI, particularly deep learning, in TMJ MRI shows promising results as a diagnostic-assistance tool to segment TMJ structures and to classify disc position. Further studies exploring more diverse and multicenter data will improve the validity and generalizability of the models before being implemented in clinical practice.