Radiology Advances Podcast | RSNA

A podcast showcasing articles from the Radiology Advances journal. Podcast Team Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH, Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD

Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery 09/17/2025

Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery A prospective study evaluates ultrasound-derived fat fraction (UDFF) as a tool to monitor hepatic steatosis after bariatric surgery. Host commentary unpacks how UDFF may offer a non-invasive, accessible, and quantitative alternative to MRI-PDFF and liver biopsy, and highlights UDFF’s clinical potential for routine liver fat surveillance. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38006945

Automated Brain Hemorrhage Segmentationwith on CT 09/03/2025

Automated Brain Hemorrhage Segmentationwith on CT A multi-center study evaluating an AI model for automated CT segmentation of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema. Host commentary highlights how the deep learning tool delivers near-expert accuracy in under 20 seconds—dramatically reducing time and enhancing precision in acute stroke care. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38006905

The Robotic Edge in CT-Guided Procedures 08/20/2025

The Robotic Edge in CT-Guided Procedures A prospective randomized trial compares robotic versus manual needle insertion for CT-guided intervention. Host commentary summarizes the results that show the robotic system matched manual accuracy and clinical success rates while significantly reducing radiation exposure to the interventionalist. The discussion touches on clinical implications for workflow, safety, and the evolving role of robotics in interventional radiology. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/37636480

AI-Powered Precision in MRI with MRAnnotator 08/06/2025

AI-Powered Precision in MRI with MRAnnotator MRAnnotator is a deep learning model that segments 44 anatomical structures across diverse MRI sequences. Developed at Mount Sinai, it shows strong generalizability across scanners and sites, outperforming existing models. Host commentary summarizes the model development and datasets and explores its impact on AI development, annotation workflows, and multi-center research. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/37636665

The Future of Breast Cancer Screening with AI 07/16/2025

The Future of Breast Cancer Screening with AI This episode explores a groundbreaking study from Radiology Advances evaluating the use of artificial intelligence as a second reader in screening mammography. Host commentary highlights how the AI-assisted workflow improved cancer detection, reduced radiologist workload, and enhanced reading efficiency, while also emphasizing the importance of thoughtful integration into clinical practice. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/37403495

Non-Contrast Dual-Energy CT for PE Detection 07/02/2025

Non-Contrast Dual-Energy CT for PE Detection In this ai generated episode of the Radiology Advances Podcast, we explore an innovative approach to detecting pulmonary embolism using dual-energy CT without intravenous contrast. Learn how electron density and Z-effective maps could offer a new option for patients with contraindications to contrast media. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/37257985

Radiology Advances Podcast | RSNA

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