Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study.

Purpose Artificial neural networks (ANNs) have been developed in the past few decades for many different applications in medical science and in biomedical research. The use of neural networks in oral and maxillofacial surgery is limited. The aim of this study was to determine the use of ANNs for the prediction of 2 subgroups of temporomandibular joint (TMJ) internal derangements (IDs) and normal joints using characteristic clinical signs and symptoms of the diseases. Materials and Methods Clinical symptoms and diagnoses of 161 patients with TMJ ID were considered the gold standard and were employed to train a neural network. After the training process, the symptoms and diagnoses of 58 new patients were used to verify the network's ability to diagnose. The diagnoses obtained from ANNs were compared with diagnoses of a surgeon experienced in temporomandibular disorders. The sensitivity and specificity of ANNs in predicting subtypes of TMJ ID were evaluated using clinical diagnosis as the gold standard. Results Eight cases evaluated as bilaterally normal in clinical examination were evaluated as normal by ANN. In detecting unilateral anterior disc displacement with reduction (ADDwR; clicking), the sensitivity and specificity of ANN were 80% and 95%, respectively. In detecting unilateral anterior disc displacement without reduction (ADDwoR; locking), the sensitivity and specificity of ANN were 69% and 91%, respectively. In detecting bilateral ADDwoR, the sensitivity and specificity of ANN were 37% and 100%, respectively. In detecting bilateral ADDwR, the sensitivity and specificity of ANN were 100% and 89%, respectively. In detecting cases of ADDwR at 1 side and ADDwoR at the other side, the sensitivity and specificity of ANN were 44% and 93%, respectively. Conclusion The application of ANNs for diagnosis of subtypes of TMJ IDs may be a useful supportive diagnostic method, especially for dental practitioners. Further research, including advanced network models that use clinical data and radiographic images, is recommended.