Deep learning in abdominopelvic digital subtraction angiography: a systematic review of interventional radiology applications.

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Bibliographic Details
Title: Deep learning in abdominopelvic digital subtraction angiography: a systematic review of interventional radiology applications.
Authors: Raskin D; Interventional Radiology, Cleveland Clinic, Cleveland, OH, USA. Electronic address: raskind@ccf.org., Klang E; Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA., Barash Y; Division of Vascular and Interventional Radiology, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Korfiatis P; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Partovi S; Interventional Radiology, Cleveland Clinic, Cleveland, OH, USA., McCarthy CJ; Division of Vascular and Interventional Radiology, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA., Nadkarni G; Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA., Collins JD; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Sorin V; Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Source: European journal of radiology [Eur J Radiol] 2025 Dec; Vol. 193, pp. 112456. Date of Electronic Publication: 2025 Sep 25.
Publication Type: Journal Article; Systematic Review
Journal Info: Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 8106411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7727 (Electronic) Linking ISSN: 0720048X NLM ISO Abbreviation: Eur J Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1872-7727
DOI:10.1016/j.ejrad.2025.112456