Deep learning (DL) is increasingly integrated into clinical workflows [1]. Interventional radiology (IR), which relies on imaging for procedural guidance, can potentially benefit from these advancements [2]. Automated image analysis, noise reduction, motion correction, and real-time anomaly detection could enhance precision. These algorithms may optimize pre-procedural planning, enable more accurate vessel segmentation, improve detection of vascular abnormalities, enable real-time guidance durin…