Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review

This systematic review aimed to evaluate the application of artificial intelligence (AI) in the identification of temporomandibular joint (TMJ) disc position in normal or temporomandibular joint disorder (TMD) individuals using magnetic resonance imaging (MRI). Database search was done in Pub med, Google scholar, Semantic scholar and Cochrane for studies on AI application to detect TMJ disc position in MRI till September 2023 adhering PRISMA guidelines. Data extraction included number of patients, number of TMJ/MRI, AI algorithm and performance metrics. Risk of bias was done with modified PROBAST tool. Seven studies were included (deep learning = 6, machine learning = 1). Sensitivity values (n = 7) ranged from 0.735 to 1, while specificity values (n = 4) ranged from 0.68 to 0.961. AI achieves accuracy levels exceeding 83%. MobileNetV2 and ResNet have revealed better performance metrics. Machine learning demonstrated the lowest accuracy 74.2%. Risk of bias was low (n = 6) and high (n = 1). Deep learning models showed reliable performance metrics for AI based detection of temporomandibular joint disc position in MRI. Future research is warranted with better standardisation of design and consistent reporting.