Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier from Lumbar Sagittal T2-Weighted MRI

Background: Accurate 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. Methods: Using 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. Results: The 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. Conclusion: DL-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.