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

Episode 21: Can AI catch cardiomegaly on chest CTs ordered for other reasons? 05/06/2026

Episode 21: Can AI catch cardiomegaly on chest CTs ordered for other reasons? This episode explores a study from the University of Texas Southwestern Medical Center and MD Anderson Cancer Center in the United States that clinically validates an FDA-cleared AI tool for measuring total cardiac volume on non-contrast, non-gated chest CT. Across 307 patients with paired echocardiography, the AI discriminated normal from abnormal cardiac volume with an AUC of 0.81 in men and 0.77 in women, and far outperformed routine radiologist sensitivity for cardiomegaly. The tool offers a tunable, reproducible opportunistic screening layer on chest CT's already being performed. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/41148320

Episode 20: Minimum Data for Maximum Accuracy 04/22/2026

Episode 20: Minimum Data for Maximum Accuracy This episode explores a study from the Emory Sports Performance and Research Center and the University of Lausanne that determined how few annotated MRI exams are needed to train a reliable deep learning model for thigh muscle segmentation. Using the nnU-Net framework with incrementally larger training sets, the researchers found that just 20 high-quality annotated subjects produced clinically acceptable segmentation across 14 thigh muscles, with biomarker agreement virtually indistinguishable from expert manual segmentation. All tools and trained models have been made openly available. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/40727450

Episode 19: Leveraging Federated Learning to Supplement an AI Learning Dataset 04/08/2026

Episode 19: Leveraging Federated Learning to Supplement an AI Learning Dataset This episode discusses a study from UCLA in the United States that used federated learning to train a deep learning model for automatic segmentation and quantification of visceral and subcutaneous abdominal fat in children using free-breathing 3D MRI. By leveraging a larger adult dataset alongside a small pediatric cohort, the model achieved strong agreement with expert manual segmentation in under three seconds per patient. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/40727305

Episode 18: Ferumoxytol MRI to detect slow gastrointestinal bleeding 03/18/2026

Episode 18: Ferumoxytol MRI to detect slow gastrointestinal bleeding This episode reviews a proof-of-concept study from Mayo Clinic Minnesota on the use of ferumoxytol-enhanced MRI for detecting gastrointestinal bleeding after a comprehensive conventional workup has been negative. We examine how this blood pool agent's prolonged intravascular half-life addresses the diagnostic challenge of slow and intermittent GI bleeding, and discuss the clinical implications for patient management. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/40316205

Episode 17: AI for labeling aortic dissection on CT for endovascular treatment planning and surveillance 03/04/2026

Episode 17: AI for labeling aortic dissection on CT for endovascular treatment planning and surveillance This episode reviews a study from the ROADMAP Group evaluating deep reinforcement learning for automatic aortic landmark localization in Stanford Type B aortic dissection — examining whether AI can match expert human performance for a task critical to treatment planning and long-term surveillance. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/40316040

Episode 16: Differentiating cysts from solid masses more reliably on breast ultrasound 02/18/2026

Episode 16: Differentiating cysts from solid masses more reliably on breast ultrasound This episode explores a technological advance from Johns Hopkins in the United States that improves diagnostic ultrasound for breast masses. By combining short-lag spatial coherence imaging with an objective metric called generalized contrast-to-noise ratio, the researchers achieved a dramatic boost in diagnostic accuracy—especially in dense breast tissue—while reducing variability among radiologists and avoiding misclassification of cancers. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39941875

Episode 15: Choroid plexus segmentation on MRI without contrast injection 02/04/2026

Episode 15: Choroid plexus segmentation on MRI without contrast injection This episode highlights a study from Korea using deep learning to generate synthetic contrast-enhanced brain MRI images—without injecting contrast agents. The model accurately segmented the choroid plexus and matched real contrast-enhanced scans in volume analysis, offering a potentially safer, scalable tool for neuroimaging. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39941845

Episode 14: Benchmarking Pancreas Segmentation on CT 01/21/2026

Episode 14: Benchmarking Pancreas Segmentation on CT This episode explores a study from Radiology Advances tackling one of AI’s toughest challenges in medical imaging: consistent pancreas segmentation across CT scans. The authors benchmarked multiple models against multi-reader human consensus and introduced a new metric, Fractional Threshold (FT), to measure robustness. Their human-in-the-loop workflow flagged just 5% of cases for expert review, matching human reliability while cutting annotation time 23-fold. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39620300

Episode 13: Making Ultrasound Elastography More Reliable 01/07/2026

Episode 13: Making Ultrasound Elastography More Reliable This episode explores a study from Radiology Advances challenging FDA's acoustic output limits for liver ultrasound elastography for obese patients. The authors tested the exam at a mechanical index of 2.5, well above the 1.9 regulatory ceiling, and found no liver injury using stringent biochemical criteria. The payoff: a 29.2% reduction in measurement variability and 40% fewer failed attempts in obese participants, potentially transforming metabolic dysfunction associated steatotic liver disease screening in the population that needs it most. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39620160

Episode 12: Deep Silicon Photon Counting CT for Liver Fat 12/17/2025

Episode 12: Deep Silicon Photon Counting CT for Liver Fat This episode features a cutting-edge study from Radiology Advances exploring Deep Silicon Photon-Counting CT (DS-IPCCT) for liver fat quantification. Using in silico models, the investigational system demonstrated high spectral accuracy, robust material decomposition, and low error rates—potentially overcoming key limitations of conventional CT and MRI. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39152795

Episode 11: RadGPT Delivers a Smarter Approach to Knee Imaging 12/03/2025

Episode 11: RadGPT Delivers a Smarter Approach to Knee Imaging This episode explores Radiology Advances research on RadGPT—a hybrid AI system combining image analysis with a language model to interpret knee radiographs. Built on 77,000 images, the system incorporates mandatory human review, dramatically improving diagnostic accuracy and report quality. Host commentary highlights its potential as a diagnostic assistant for trainees and an efficiency tool for experts. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/39152495

Episode 10: Deep Learning for Faster Neuro MRI 11/12/2025

Episode 10: Deep Learning for Faster Neuro MRI This episode covers a study from Radiology Advances evaluating deep learning–accelerated MRI across routine neuroradiology exams. Using Siemens’ Deep Resolve, scan times were cut by over 50% without sacrificing diagnostic image quality. Host commentary explores reader preferences, artifacts, and when DL-MRI may be best suited for clinical use. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38824730

Episode 9: CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation 10/29/2025

Episode 9: CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation This episode discusses a study from Radiology Advances evaluating contrast-enhanced CT as a non-invasive alternative for lung shunt fraction (LSF) estimation in hepatic radioembolization to the current standard, 99mTc-MAA nuclear medicine imaging. The proposed CT-based method showed strong correlation with standard MAA-based LSF, offering a faster, safer, and potentially more accurate planning approach without compromising clinical decision-making. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38824525

Episode 8: Advancing MRI Efficiency in Memory Disorders 10/15/2025

Episode 8: Advancing MRI Efficiency in Memory Disorders This episode covers a study in Radiology Advances evaluating deep learning–accelerated T1 MPRAGE MRI in patients with memory loss. The approach cut scan time by more than half while preserving image quality and measurement accuracy—offering faster, more comfortable imaging for dementia care and longitudinal follow-up. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38360285

Episode 7: Automating Myocardial Infarct Segmentation 10/01/2025

Episode 7: Automating Myocardial Infarct Segmentation This episode spotlights a study from Radiology Advances introducing a fully automated deep learning pipeline for myocardial infarct segmentation on late gadolinium enhancement cardiac MRI. Developed at the Medical University of Innsbruck, the model showed near-perfect agreement with human experts and even outperformed manual segmentations in blinded qualitative review. /episode/index/show/2d51c0e2-8968-4a75-a4b4-134eb5cd74f3/id/38359980

Episode 6: Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery 09/17/2025

Episode 6: 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

Episode 5: Automated Brain Hemorrhage Segmentationwith on CT 09/03/2025

Episode 5: 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

Episode 4: The Robotic Edge in CT-Guided Procedures 08/20/2025

Episode 4: 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

Episode 3: AI-Powered Precision in MRI with MRAnnotator 08/06/2025

Episode 3: 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

Episode 2: The Future of Breast Cancer Screening with AI 07/16/2025

Episode 2: 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

Episode 1: Non-Contrast Dual-Energy CT for PE Detection 07/02/2025

Episode 1: 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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