ArticleSpine2026
Automated Detection of Cervical Spinal Cord Compression on MRI Using YOLO11 Deep Learning Architecture: A Two-Center External Validation Study.
Article in Spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designA retrospective two-center external validation study was conducted at two medical centers, collecting cervical spine MRI data from patients suspected of degenerative cervical myelopathy (DCM) between March 2022 and August 2024, forming a consecutive series with external validation.
objectiveTo develop and validate a deep learning model utilizing YOLO11 architecture for automated detection of cervical spinal cord compression on MRI and evaluate its performance against expert annotations. SUMMARY OF BACKGROUND DATA: DCM represents the leading cause of nontraumatic spinal cord injury in adults. While MRI facilitates early detection and provides the foundation for timely intervention, image interpretation remains subjective and dependent on physician experience, resulting in diagnostic variability and challenges in clinical consistency. MATERIALS AND
methodsA YOLO11-based deep learning model was implemented with a binary classification scheme (Normal vs . Compression). Five physicians annotated 1431 sagittal T2-weighted cervical MRI images from 735 patients using standardized protocols, achieving excellent interobserver agreement. The data set comprised training/validation sets (577 patients, 1141 images), an internal test set (64 patients, 115 images), and an external test set (94 patients, 175 images). Five-fold cross-validation assessed model robustness. Standardized preprocessing incorporating contrast enhancement, noise reduction, and normalization was applied. Gradient-weighted Class Activation Mapping enhanced model interpretability.
resultsFive-fold cross-validation yielded consistent performance with mAP50 ranging from 0.917 to 0.970, precision from 0.897 to 0.923, and recall from 0.922 to 0.946. External testing demonstrated statistically superior agreement with expert annotations (mAP50=0.944, 95% CI: 0.934-0.953) compared with mid-level physician annotations (mAP50=0.912, 95% CI: 0.908-0.919), with the difference being statistically significant (95% CI of difference: 0.015-0.043, P <0.05).
conclusionThe YOLO11-based model demonstrated stable two-center performance with close alignment to expert-level clinical standards. The rapid inference, high sensitivity, and integrated visualization system address key challenges related to efficiency and interpretability in clinical AI applications for cervical spinal cord compression assessment.
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