Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network

Abstract

This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. To this end, 166 skull images of patients aged > 16 years with trigeminal neuralgia diagnoses were compiled into a labeled trigeminal neuralgia dataset and 498 skull images of patients with unruptured intracranial aneurysms were compiled into a labeled control dataset. The images were partitioned into training, validation, and test datasets in a 6:2:2 ratio using random permutation. The accuracy and area under the receiver-operating characteristic (AUROC) curve were used to evaluate the classifier performance. Gradient-weighted class activation mapping was employed to identify the focal areas of attention. External validation was performed using a dataset obtained from another institution. We observed an overall accuracy of 87.2%, sensitivity and specificity of 0.72 and 0.91, respectively, and AUROC of 0.90 on the test dataset. In most cases, trigeminal neuralgia was predicted by observing the sphenoid body and clivus. The overall accuracy on the external test dataset was 71.0%, indicating the promise of deep learning-based models in distinguishing between X-ray skull images of patients with trigeminal neuralgia and control individuals. This is expected to serve as a useful screening tool after further development.