Computational Phenotypes for Temporomandibular and Orofacial Pain Disorders

Background: Temporomandibular disorders (TMDs) and orofacial pain (OFP) conditions affect approximately one-third of the global population, yet diagnosis often relies on subjective clinical assessment rather than standardized, evidence-based criteria. This diagnostic uncertainty contributes to misdiagnosis, misdirected or delayed treatment and significant healthcare costs. Case description: We prospectively collected structured clinical data from 1,584 patients using a custom, structured, note-documentation system designed for machine learning compatibility (Smart Medical Note [SmartNote]). From the 77 possible TMD-OFP diagnoses, we identified 17 conditions with sufficient case volumes ([≥]24 exemplars) for analysis. The clinical features which were present at or above >50% in these cases underwent Monte Carlo statistical analysis to determine which ones are most useful for creating an objective diagnostic profile. The 17 evidence-based diagnostic profiles demonstrated strong discriminative performance (median AUC values 0.71-0.99). Practical Implications: These diagnostic profiles represent the first large-scale, statistically derived criteria for TMD and orofacial pain conditions. By providing objective, quantifiable diagnostic standards, this statistical analysis of structured electronic records can support algorithmic diagnostic-assist software, reduce the time to appropriate treatment, and enhance clinical decision-making and documentation of diagnoses for dental practitioners, regardless of their level of specialized training in orofacial pain. Diagnostic alignment analysis confirms complete concordance with DC-TMD criteria for seven major diagnostic categories, validating the clinical applicability of our data-driven approach.