Deep learning in abdominopelvic digital subtraction angiography: a systematic review of interventional radiology applications
Preliminary studies show that DL can improve hemorrhage detection, image quality, and vessel segmentation in DSA. However, larger, prospectively validated datasets are warranted. Currently no FDA-approved DL tools exist for abdominal or pelvic DSA. Future efforts should explore advanced generative AI and multimodal approaches.
Authors
- Vera Sorin (ORCID: https://orcid.org/0000-0003-0509-4686)
- Sasan Partovi (ORCID: https://orcid.org/0000-0001-9583-5083)
- Daniel Raskin (ORCID: https://orcid.org/0000-0003-3584-3748)
- Jeremy D. Collins (ORCID: https://orcid.org/0000-0003-0707-1736)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Panagiotis Korfiatis (ORCID: https://orcid.org/0000-0003-2516-1751)
- Yiftach Barash (ORCID: https://orcid.org/0000-0002-7242-1328)
- Colin J. McCarthy
Institutions
- Beth Israel Deaconess Medical Center (US)
- Cleveland Clinic (US)
- Mayo Clinic in Arizona (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- European Journal of Radiology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1016/j.ejrad.2025.112456
- Primary Topic
- Radiology practices and education
- Type
- article
- Field-Weighted Citation Impact
- 0.00