Diagnostic Imaging's Top Five AI Content of 2024

In external validation testing, a deep learning model demonstrated an average AUC of 87.6 percent for detecting clinically significant prostate cancer (csPCA) on prostate MRI for patients with PI-RADS 3 assessments.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

Designed for advanced OB-GYN applications, the Samsung Z20 ultrasound platform reportedly provides a combination of artificial intelligence (AI) tools and enhanced ergonomics.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

For patients previously assessed with LI-RADS LR-4 and LR-M presentations based on MRI or CT findings, the use of contrast-enhanced ultrasound led to 30 percent of these patients having LR-5 assessments definitive for hepatocellular carcinoma.

An emerging deep learning radiomics model based on biparametric MRI (bpMRI) offered a 14 to 17 percent higher AUC range than PI-RADS scoring for predicting the aggressiveness of prostate cancer, according to new research findings.