Deep learning and radiomics-driven algorithm for automated identification of May-Thurner syndrome in Iliac CTV imaging
This study presents a comprehensive approach to automate MTS detection from Iliac CTV scans, combining deep learning and radiomics. The results suggest the potential clinical utility of the developed model in diagnosing MTS, offering a non-invasive and efficient alternative to traditional methods.
Authors
- Chengzhi Qiu (ORCID: https://orcid.org/0000-0002-5228-9104)
- Lufeng Chen (ORCID: https://orcid.org/0000-0002-6357-8112)
- Donglin Li (ORCID: https://orcid.org/0000-0002-4221-8564)
- Hua-Feng Zheng
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-29
- DOI
- https://doi.org/10.3389/fmed.2025.1526144
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00