Automated Classification of Cervical Spinal Stenosis Using Deep Learning on Computed Tomography Scans

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Multiclass accuracy: 88.74-89.40%; Binary classification accuracy: 94.70-96.03%

• Primary Outcome: Diagnostic accuracy for cervical spinal stenosis classification

• Nnt Or Sensitivity: Sensitivity and specificity not reported; >90% consistency with senior physicians for 3/4 models

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and technical expertise for implementation Relevant for rural/remote areas with limited MRI access; potential Medicare Benefits Schedule implications for CT-based diagnosis; alignment with RANZCR imaging guidelines needed Patients requiring cervical spine imaging where MRI is contraindicated or unavailable

Abstract

STUDY DESIGN: Retrospective study. OBJECTIVE: To develop and validate a computed tomography-based deep learning (DL) model for diagnosing cervical spinal stenosis (CSS). BACKGROUND: Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize health care resource allocation. MATERIALS AND METHODS: Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The 2-stage model architecture employed: (1) A Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI), (2) Comparison of 16 Convolutional Neural Network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience. RESULTS: In the multiclass classification task, 4 high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians. In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All 4 models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, 3 models (excluding DL4-26d) exhibited consistency rates exceeding 90%. CONCLUSIONS: The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.

References

1. 1.Zhang, Y.-L., Huang, J.-W., Li, K.-Y., Li, H.-L., Lin, X.-X., Ye, H.-B., Chen, Y.-H., & Tian, N.-F. (2026). Automated classification of cervical spinal stenosis using deep learning on computed tomography scans. Spine. https://doi.org/10.1097/BRS.0000000000005414