Haematologic Data Improves Long-Term Prediction Accuracy of Artificial Intelligence Models for Temporomandibular Disorders

Objectives: This study aimed to develop and evaluate an artificial intelligence (AI) model to predict long-term treatment outcomes in temporomandibular disorder (TMD) patients using clinical data and verify the value of adding haematologic data in enhancing predictive accuracy. Methods: The medical records of 132 TMD patients who visited the clinic and underwent 6 months of non-invasive conservative treatment between 2013 and 2019 were included in this study. The clinical data and haematologic features were collected from medical records. A decision tree algorithm was employed for feature selection, followed by a deep neural network (DNN) to build the prediction model. The performance of the models based on the decision tree algorithm and DNN was evaluated. Results: The decision tree model achieved an accuracy of 90.6% and an F1-score of 0.800. The subjective pain-related features, along with haematologic markers associated with systemic inflammation, were proven to be important features in the decision tree model. The predictive performance of the DNN model improved as haematologic features were added, with the final model achieving an accuracy of 90.6% and an F1-score of 0.769. Conclusions: This study showed the potential of machine learning models in predicting long-term TMD prognosis using clinical and haematological features. In addition, these findings highlight the importance of including both subjective pain assessments and systemic haematologic markers for the development of aetiology-based diagnostic systems for TMD to enhance clinical decision-making and prognosis prediction accuracy.