Neurosurgery 97:993–1002, 2025
This study presents development and validation of an AI-based diagnostic decision support tool that distinguishes temporomandibular disorders (TMDs) from trigeminal neuralgia (TN) using a standardized facial pain questionnaire and targeted orofacial examination. Supervised machine learning models (Random Forest, Logistic Regression, SVM) were trained on data from 101 patients, with the Random Forest achieving the best performance (≈90% accuracy; ROC-AUC ~0.95).
The analysis identifies clinically interpretable predictors—TMJ and masticatory muscle tenderness favor TMD, while brief electric-shock–like pain and prior response to trigeminal surgery favor TN—and evaluates class imbalance effects and limitations for clinical deployment. The work emphasizes the need for external validation, cautious integration into workflows, and balanced training to improve generalizability.
• Differentiation Challenge: Temporomandibular disorders (TMDs) and trigeminal neuralgia (TN) both cause orofacial pain but require very different treatments, making accurate diagnosis crucial; TMDs are far more common and often misdiagnosed as TN, leading to inappropriate management.
• AI Diagnostic Tool: A machine learning (ML) model using questionnaire data and physical examination can reliably distinguish TMD from TN with approximately 90% accuracy, with a Random Forest Classifier showing the best performance (F1 score up to 0.953).
• Key Predictive Features: The most important diagnostic indicators are TMJ tenderness and masticatory muscle tenderness (favoring TMD), and brief, unpredictable, electric shock–like pain episodes (favoring TN).
• Data Collection: Comprehensive data—including both patient-reported symptoms and structured physical examination—significantly improves diagnostic accuracy compared to using only a subset of features.
• Prevalence and Misdiagnosis: TMDs affect 5–12% of the population, while TN is much rarer (0.03–0.3%); the high prevalence of TMD means misdiagnosis as TN is a significant concern, with many patients meeting criteria for TN2 possibly having TMD instead.
• Model Robustness: Training ML models on balanced datasets (even when real-world prevalence is imbalanced) improves accuracy and reduces false positives for the minority class (TN).
• Clinical Utility: The AI tool provides transparent, interpretable results that align with clinical reasoning, supporting clinicians in differentiating between TMD and TN, but external validation in diverse populations is needed before routine clinical adoption.
• Limitations: Further research is required for external validation, integration into workflows, and to address potential algorithmic bias; overreliance on algorithmic output should be avoided in favor of combined clinical expertise.
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