Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier from Lumbar Sagittal T2-Weighted MRI
Authors
Affiliations (1)
Affiliations (1)
- Department of Health Sciences and Technology, ETH Zurich, Zurich, 8092, Switzerland
Abstract
BackgroundAccurate assessment of the severity of central canal stenosis (CCS) on lumbar spine MRI is critical for clinical decision-making. We evaluated deep learning models for automated CCS grading on sagittal T2-weighted MRI, focusing on uncertainty quantification to improve clinical reliability. MethodsUsing a retrospective cohort from the LumbarDISC dataset (1,974 patients), we compared multiple deep learning architectures for three-level CCS classification (normal / mild, moderate, severe). To assess model confidence, Monte Carlo (MC) dropout and Test Time Augmentation (TTA) techniques were applied to quantify prediction uncertainty. ResultsThe fine-tuned Spinal Grading Network (SGN) achieved a balanced accuracy of 79.4% and a macro F1 score of 68.8%, with per-class accuracies of 71.3% for moderate and 78.5% for severe stenosis. MC dropout revealed an increase in uncertainty predominantly in moderate and severe cases, while TTA uncertainty was higher for mild stenosis. ConclusionDL-based CCS grading demonstrates potential to assist radiologists by providing rapid, standardized evaluations. Incorporating uncertainty quantification offers a safeguard to flag ambiguous cases, thus supporting clinical trust and facilitating safer integration of AI tools into the interpretation of spine MRI.