Application of artificial intelligence in the diagnosis and management of temporomandibular joint osteoarthritis using cone-beam computed tomography: An evidence-based systematic review.

Abstract

Purpose: Temporomandibular joint osteoarthritis (TMJOA) is a significant subtype of temporomandibular joint disorders (TMDs). The purpose of this study was to comprehensively summarize the current literature on the use of artificial intelligence (AI) technologies in the diagnosis and management of TMJOA using cone-beam computed tomography (CBCT).

Materials and methods: This systematic review was pre-registered in the PROSPERO database (PROSPERO CRD42024509772). Up to December 2023, research was conducted using Google Scholar, Embase, MEDLINE, and Web of Science databases to identify studies evaluating the use of AI technologies in the management and diagnosis of TMJOA via CBCT. The search strategy included MeSH terms, keywords, and their combinations. Risk of bias was assessed using the ROBINS-I tool.

Results: Out of 2,543 articles retrieved, a total of 9 studies were included in this systematic review. All included studies were observational and employed AI models based on convolutional neural networks, including SVA, SSD, LightGBM, XGBoost, and YOLO. The performance of these models varied, with accuracy ranging from 73.5% to 99% and F1-scores between 0.80 and 0.86. Among these, YOLO demonstrated the highest accuracy for the assessment and diagnosis of TMJOA using CBCT scans.

Conclusion: AI algorithms developed for the automated diagnosis of TMJOA can be utilized by clinicians as decision-support tools. Incorporating diverse input data types, such as electronic medical records, radiomics features, and biomarkers, alongside diagnostic imaging may further increase the diagnostic accuracy for TMDs.