Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI

This study aims to establish a large degenerative cervical myelopathy cohort and develop deep learning models for predicting cervical canal stenosis from sagittal T2-weighted MRI.

Data was collected retrospectively from patients who underwent a cervical spine MRI from January 2007 to December 2022 at a single institution. Ground truth labels for cervical canal stenosis were obtained from sagittal T2-weighted MRI using Kang's grade, a four-level scoring system that classifies stenosis with the degree of subarachnoid space obliteration and cord indentation. ResNet50, VGG16, MobileNetV3, and EfficientNetV2 were trained using threefold cross-validation, and the models exhibiting the largest area under the receiver operating characteristic curve (AUC) were selected to produce the ensemble model. Gradient-weighted class activation mapping was adopted for qualitative assessment. Models that incorporate demographic features were trained, and their corresponding AUCs on the test set were evaluated.

Of 8676 patients, 7645 were eligible for developing deep learning models, where 6880 (mean age, 56.0 ± 14.3 years, 3480 men) were used for training while 765 (mean age, 56.5 ± 14.4 years, 386 men) were set aside for testing. The ensemble model exhibited the largest AUC of 0.95 (0.94-0.97). Accuracy was 0.875 (0.851-0.898), sensitivity was 0.885 (0.855-0.915), and specificity was 0.861 (0.824-0.898). Qualitative analyses demonstrated that the models accurately pinpoint radiologic findings suggestive of cervical canal stenosis and myelopathy. Incorporation of demographic features did not result in a gain of AUC.

We have developed deep learning models from a large degenerative cervical myelopathy cohort and thoroughly explored their robustness and explainability.