Deep learning for temporomandibular joint arthropathies: A systematic review and meta‐analysis

Abstract

AbstractBackground and ObjectiveThe accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed‐upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies.Materials and MethodsAn electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint‐based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS‐2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc.ResultsFull text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS‐2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%–100%, Dice coefficient: 85%–98%, and AUC: 77%–99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta‐analysis. The pooled sensitivity was 95% (85%–99%), specificity: 92% (86%–96%), and AUC: 97% (96%–98%). DORs were 232 (74–729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present.ConclusionDeep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence‐based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.