Machine-learning–based detection of degenerative temporomandibular joint diseases using lateral cephalograms

Abstract

Degenerative temporomandibular joint diseases (DJDs) are common diseases in dental practice, characterized by a series of degenerative processes in the temporomandibular joint. Early clinical detection of DJD by dental practitioners can be beneficial to prevent or alleviate the further progression of the disease. This study aimed to develop a cephalogram-based multidimensional nomogram to screen DJD. A total of 502 patients (170 normal and 332 with DJD) were randomly assigned to a training set (n= 351) or a validation set (n= 151). Thirty-six cephalometric parameters were extracted from the cephalograms to be used as input for a predictive machine-learning algorithm. Multivariable logistic regression was used to construct a combined model for visualization in the form of a nomogram. Receiver operating characteristic curve, calibration testing, and decision curve analyses were conducted to evaluate the performance of the combined model. A Ceph score consisting of 22 cephalometric parameters were significantly associated with DJD (P<0.01). A combined model that consisted of Ceph scores and clinical features (including age, gender, limited mouth opening, crepitus, etc.) performed well in the receiver operating characteristic curve (area under the curve, 0.893), calibration test, and decision curve analyses, indicating its potential clinical value. This study constructed and verified a multidimensional nomogram consisting of Ceph scores and clinical features, which may contribute to the clinical screening of DJD in dental practice. Future studies are needed to test the reliability of the model with similar parameters.