Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study
This study developed a robust predictive model that integrates clinical features and imaging data to identify high-risk patients for CS following lumbar fusion. This model has the potential to improve clinical decision-making and reduce the need for revision surgeries, easing the burden on healthcare systems.
Authors
- Congying Zou (ORCID: https://orcid.org/0009-0001-5268-3638)
- Lei Zang (ORCID: https://orcid.org/0000-0003-1403-4159)
- Ruiyuan Chen (ORCID: https://orcid.org/0000-0002-5891-8717)
- Fei Qi (ORCID: https://orcid.org/0000-0002-5364-4235)
- Hongxing Song
- Biao Wang (ORCID: https://orcid.org/0000-0001-6253-2713)
Institutions
- Capital Medical University (CN)
- Beijing Shijingshan Hospital (CN)
- Beijing Friendship Hospital (CN)
- Beijing Shijitan Hospital (CN)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2025-03-02
- DOI
- https://doi.org/10.1186/s12938-025-01355-y
- Citations
- 4
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.70
Funders
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support