European journal of radiology · 2025
Deep learning in abdominopelvic digital subtraction angiography: a systematic review of interventional radiology applications.
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Raskin Daniel, Klang Eyal, Barash Yiftach, Korfiatis Panagiotis, Partovi Sasan, McCarthy Colin J, Nadkarni Girish, Collins Jeremy D, Sorin Vera
Аннотация
PURPOSE: Deep learning (DL) is increasingly explored in interventional radiology (IR) applications. This systematic review evaluates current DL-based applications for digital subtraction angiography (DSA) in abdominopelvic interventions, summarizes performance, and identifies gaps in the literature.
MATERIALS AND METHODS: Following PRISMA guidelines, we searched MEDLINE, Scopus, and Google Scholar for studies published up to February 1, 2025. English-language original articles assessing DL methods for automatic DSA image analysis were included, and study quality was evaluated with QUADAS-2.
RESULTS: Nine studies were included. Two focused on hemorrhage detection, in which area under the curve (AUC) values ranged between 0.80-0.85. Four examined image enhancement, one performed vessel segmentation, and one applied classification of the anatomic location. Only a single study evaluated treatment response prediction, with an accuracy of 0.75. Most models were tested on small datasets from single centers, limiting their generalizability.
CONCLUSION: 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.
MATERIALS AND METHODS: Following PRISMA guidelines, we searched MEDLINE, Scopus, and Google Scholar for studies published up to February 1, 2025. English-language original articles assessing DL methods for automatic DSA image analysis were included, and study quality was evaluated with QUADAS-2.
RESULTS: Nine studies were included. Two focused on hemorrhage detection, in which area under the curve (AUC) values ranged between 0.80-0.85. Four examined image enhancement, one performed vessel segmentation, and one applied classification of the anatomic location. Only a single study evaluated treatment response prediction, with an accuracy of 0.75. Most models were tested on small datasets from single centers, limiting their generalizability.
CONCLUSION: 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.