Using machine learning to classify temporomandibular disorders: a proof of concept

Abstract

Background: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic process of temporomandibular disorders (TMD) as a potential solution to mitigate diagnostic disparities associated with this condition. Objectives: In this study, we evaluated a machine-learning model for classifying TMDs based on the International Classification of Orofacial Pain, using structured data. Methodology: Model construction was performed by the exploration of a dataset comprising patient records from the repository of the Multidisciplinary Orofacial Pain Center (CEMDOR) affiliated with the Federal University of Santa Catarina. Diagnoses of TMD were categorized following the principles established by the International Classification of Orofacial Pain (ICOP-1). Two independent experiments were conducted using the decision tree technique to classify muscular or articular conditions. Both experiments uniformly adopted identical metrics to assess the developed model's performance and efficacy. Results: The classification model for joint pain showed a relevant potential for general practitioners, presenting 84% accuracy and f1-score of 0.85. Thus, myofascial pain was classified with 78% accuracy and an f1-score of 0.76. Both models used from 2 to 5 clinical variables to classify orofacial pain. Conclusion: The use of decision tree-based machine learning holds significant support potential for TMD classification.