Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI
We have developed deep learning models from a large degenerative cervical myelopathy cohort and thoroughly explored their robustness and explainability.
Authors
- Hyoungmin Kim (ORCID: https://orcid.org/0000-0002-4500-9653)
- Sam Yeol Chang (ORCID: https://orcid.org/0000-0003-4152-687X)
- Bong‐Soon Chang (ORCID: https://orcid.org/0000-0002-8992-2559)
- Sung Cheol Park (ORCID: https://orcid.org/0000-0001-9389-5429)
- Wounsuk Rhee (ORCID: https://orcid.org/0000-0001-7770-5760)
Institutions
- University of Illinois Urbana-Champaign (US)
- Government of the Republic of Korea (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- National University College (PR)
- Bumin Hospital Group (KR)
Publication Details
- Journal
- Skeletal Radiology
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1007/s00256-025-04917-2
- Citations
- 3
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Seoul National University