Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning
The DL model could fully automatically and reliably assess cervical canal stenosis using MRI scans.
Authors
- Yaying Zhang (ORCID: https://orcid.org/0000-0001-9552-3274)
- Kai Cao (ORCID: https://orcid.org/0000-0003-4972-108X)
- Chao Ma (ORCID: https://orcid.org/0000-0002-8339-0176)
- Lin Zhu (ORCID: https://orcid.org/0000-0002-3877-8489)
- Peiyan Zhang (ORCID: https://orcid.org/0000-0002-8691-1846)
- Xiaoqiang Miao (ORCID: https://orcid.org/0009-0005-4765-7928)
- Ning Zhang (ORCID: https://orcid.org/0000-0002-1374-3052)
- Jiacheng Yang (ORCID: https://orcid.org/0000-0003-2898-9538)
- Kang Yan
- Mu Lu (ORCID: https://orcid.org/0000-0003-1824-9798)
- Yueyi Zhang (ORCID: https://orcid.org/0000-0003-0788-8826)
- Yu Luo (ORCID: https://orcid.org/0000-0001-8044-7096)
- Xiaochen Feng (ORCID: https://orcid.org/0000-0003-3239-0317)
Institutions
- Second Military Medical University (CN)
- Changhai Hospital (CN)
- Shenzhen Technology University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-08-21
- DOI
- https://doi.org/10.21037/qims-2025-67
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00