Assisting in Diagnosis of Temporomandibular Disorders: A Deep Learning Approach

Abstract

The etiology of Temporomandibular disorders (TMD) is still unclear, and its symptoms, signs, and progression are extremely complex. TMD requires early diagnosis and treatments, especially for combinations with other oral diseases. The research targets at developing an artificial neural network (ANN) model for predicting TMD based on clinical-collected data including clinical features, systematic medical condition, and psychosocial state. The popular data mining-based ANN was utilized to predict TMD with all 18 variables collected from patients as the input. The total dataset consists of 88 cases which were reviewed by Board-certificated orthodontists. 75% (66) cases are randomly selected as the training dataset, while the remaining 25% (22) cases are for test. Among the considered 88 cases, 58 (65.9%) were with TMD, while the left 30 (34.1%) without TMD. The numbers of male and female were 21 and 67, respectively, while the average age was 27.63 years. The calculated average sensitivity and specificity of ANN-based TMD risk predictions through 10-fold-cross-validation analysis were 92.31% (95% confidence interval (CI), 62.09%-99.60%) and 88.89% (95% CI, 50.67%-99.42%), respectively. Moreover, the accuracy rate of ANN was 90.91% (95% CI, 78.90%-100.00%). The results show the proposed ANN model could predict the TMD risks with a high accuracy rate, indicating the potential of machine learning in oral and maxillofacial diseases screening and diagnosis, which was further illustrated in a comparison with two doctors. This study can help dental care providers to find individuals’ risk of TMD by inputting patient’s psychological factors, oral examinations, and systemic medical conditions to the developed artificial intelligence (AI) model.

Keywords

• Temporomandibular disorders
• machine learning
• disease prediction
• diagnosis