Statement of problem
The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking.
The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion.
Material and methods
Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute’s Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist.
Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience.
Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.
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Published online: February 17, 2023
Publication stageIn Press Corrected Proof
Funding: Supported by the University of Adelaide Kwok Paul Lee Bequest Project ID 75131603.
© 2023 by the Editorial Council for the Journal of Prosthetic Dentistry.