Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review
Date
2025-01
Authors
Manek, Mitul
Filipe Bezerra Silva, Diego
de Melo, Daniela Pita
Major, Paul W
Jaremko, Jacob L
T Almeida, Fabiana
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Dentomaxillofacial Radiology
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Article
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Abstract
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 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or 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 the 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 classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.
Description
Keywords
temporomandibular joint disc, magnetic resonance imaging, artificial intelligence, systematic review
Citation
Mitul Manek, Ibraheem Maita, Diego Filipe Bezerra Silva, Daniela Pita de Melo, Paul W Major, Jacob L Jaremko, Fabiana T Almeida, Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review, Dentomaxillofacial Radiology, Volume 54, Issue 1, January 2025, Pages 1–11, https://doi.org/10.1093/dmfr/twae055
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DOI
https://doi.org/10.1093/dmfr/twae055