A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation
The authors designed a novel DL system that demonstrated promising performance, especially in sensitivity, for automated diagnosis and grading of different types of lumbar spinal stenosis using spine MRI. The reliability of the system was better than that of spine surgeons. The authors' system may serve as a triage tool for LSS to reduce misdiagnosis and optimize routine processes in clinical work.
Authors
- Ning Fan (ORCID: https://orcid.org/0000-0003-0095-9476)
- Lei Zang (ORCID: https://orcid.org/0000-0003-1403-4159)
- Aobo Wang (ORCID: https://orcid.org/0000-0002-3271-1953)
- Zhao Gu
- Shuo Yuan (ORCID: https://orcid.org/0009-0002-0725-5244)
- Ruiyuan Chen (ORCID: https://orcid.org/0000-0002-5891-8717)
- Qichao Wu
- Tianyi Wang (ORCID: https://orcid.org/0000-0002-7923-3717)
- Xingyu Liu (ORCID: https://orcid.org/0000-0003-3190-251X)
- Shujun J. Zhang (ORCID: https://orcid.org/0000-0001-6139-6887)
- Peijun Du (ORCID: https://orcid.org/0000-0002-2488-2656)
- Fei Qi (ORCID: https://orcid.org/0000-0002-8635-2810)
- Xi Yu (ORCID: https://orcid.org/0000-0001-5750-7003)
Institutions
- Capital Medical University (CN)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Beijing Friendship Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Neurosurgical FOCUS
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3171/2025.4.focus24670
- Citations
- 2
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.35