Automated Detection of Cervical Spinal Cord Compression on MRI Using YOLO11 Deep Learning Architecture
The 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.
Authors
- X H Li
- Rao Fu (ORCID: https://orcid.org/0009-0007-7295-9260)
- Qingde Wa (ORCID: https://orcid.org/0000-0002-0763-7827)
- Zhijun Xin (ORCID: https://orcid.org/0000-0002-0364-5432)
- Cao Guangru
- Yuancheng Zhang (ORCID: https://orcid.org/0000-0002-9377-9281)
- Zhou Zhi-yu
- Qian Du
- Yonghu Chang
- Xinxin Shao
- Weijun Kong
- Libo Feng
- Jiaxiang Zhou
Institutions
- Sun Yat-sen University (CN)
- Zunyi Medical University (CN)
- The Seventh Affiliated Hospital of Sun Yat-sen University (CN)
- First People’s Hospital of Zunyi (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Affiliated Hospital of Zunyi Medical College (CN)
Publication Details
- Journal
- Spine
- Published
- 2026-02-03
- DOI
- https://doi.org/10.1097/brs.0000000000005639
- Primary Topic
- Cervical and Thoracic Myelopathy
- Type
- article
- Field-Weighted Citation Impact
- 0.00