Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network.
Authors
Affiliations (7)
Affiliations (7)
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, 13620, South Korea.
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, 03080, South Korea.
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, 13620, South Korea.
- TALOS Corp, 160 Yeoksam‑ro, Gangnam‑gu, Seoul, 06249, South Korea.
- Department of Neurosurgery, Ajou University School of Medicine, 164 World Cup-ro, Yeongtong-gu, Suwon, Gyeonggi-do, 16499, South Korea. [email protected].
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. [email protected].
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, 03080, South Korea. [email protected].
Abstract
This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. A labeled dataset of 166 skull images from patients aged over 16 years with trigeminal neuralgia was compiled, alongside a control dataset of 498 images from patients with unruptured intracranial aneurysms. The images were randomly partitioned into training, validation, and test datasets in a 6:2:2 ratio. Classifier performance was assessed using accuracy and the area under the receiver operating characteristic (AUROC) curve. Gradient-weighted class activation mapping was applied to identify regions of interest. External validation was conducted using a dataset obtained from another institution. The CNN achieved an overall accuracy of 87.2%, with sensitivity and specificity of 0.72 and 0.91, respectively, and an AUROC of 0.90 on the test dataset. In most cases, the sphenoid body and clivus were identified as key areas for predicting trigeminal neuralgia. Validation on the external dataset yielded an accuracy of 71.0%, highlighting the potential of deep learning-based models in distinguishing X-ray skull images of patients with trigeminal neuralgia from those of control individuals. Our preliminary results suggest that plain x-ray can be potentially used as an adjunct to conventional MRI, ideally with CISS sequences, to aid in the clinical diagnosis of TN. Further refinement could establish this approach as a valuable screening tool.