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Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

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  • Nayansi Jha
  • Kwang-sig Lee
  • Yoon-Ji Kim

Abstract

Background: Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. Objective: This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. Materials and methods: The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. Results: A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76–0.99), I2 = 97% (95% CI 0.96–0.98), p

Suggested Citation

  • Nayansi Jha & Kwang-sig Lee & Yoon-Ji Kim, 2022. "Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0272715
    DOI: 10.1371/journal.pone.0272715
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    1. Ann-Kristin Becker & Marcus Dörr & Stephan B Felix & Fabian Frost & Hans J Grabe & Markus M Lerch & Matthias Nauck & Uwe Völker & Henry Völzke & Lars Kaderali, 2021. "From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-21, February.
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