Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs.

Although magnetic resonance imaging (MRI) is the gold standard for diagnosing degenerative cervical myelopathy (DCM), its cost and limited availability can delay diagnosis. Deep learning (DL) models with convolutional neural networks (CNNs) may offer a screening alternative with plain radiographs. We aimed to develop a CNN-based DL algorithm to predict spinal cord compression (SCC) using dynamic cervical lateral radiographs (flexion, neutral, extension), perform multi-institutional validation, and identify potential causal features using gradient-weighted class activation mapping (Grad-CAM) analysis. 7878 patients who underwent both cervical radiographs and MRI at a tertiary center were labeled as SCC or control based on T2-weighted sagittal MR images and assigned to training (80.0%), validation (10.0%), and test (10.0%) sets. Ten ImageNet-pretrained architectures were trained on single-position, combined, and combined-plus-demographics models. External validation included 575 patients from an independent community hospital. The combined model with VGG-16 achieved the highest area under the receiver operating characteristic curve of 0.888 internally and 0.820 externally. Grad-CAM highlighted regions that may correspond to disc herniations, osteophytes, ossification of the posterior longitudinal ligament, and segmental instability. These results suggest that our algorithm may serve as a cost-effective screening tool with the potential to enhance diagnostic efficiency and clinical outcomes in DCM.