Group to Watch 2015: Riverside Radiology and Interventional Associates

The use of adjunctive bpMRI-based AI led to 10 percent and greater increases in lesion-level PPV for csPCa and PCa with a threshold of PI-RADS > 3.

In the first of a multi-part podcast episode, Stamatia Destounis, MD, Emily Conant, MD and Habib Rahbar, MD, share their insights on the role of abbreviated breast MRI in breast screening.

Noting a 7.4 percent incidence of motion artifacts on brain MRI scans for suspected stroke patients, the authors of a new study found that motion artifacts can reduce radiologist and AI accuracy for detecting hemorrhagic lesions.

In a second part of a new podcast episode on recently published research on projected radiation-induced cancers from computed tomography (CT) scans, Mahadevappa Mahesh, MS, Ph.D., and Joseph Cavallo, M.D., offer current perspectives on cardiac CT dosing, AI advances and the importance of teamwork in ensuring appropriate dosing for CT.

A fully convolutional data description (FCDD) model for identifying anomalies on breast MRI demonstrated an 84 percent AUC for detection tasks in a balanced cohort with a 20 percent malignancy prevalence and a 72 percent AUC for detection tasks in an imbalanced group with a 1.85 percent cancer prevalence.

A cloud-based and AI-native radiology operating system, MosaicOS reportedly enables the combination of diagnostic AI tools and workflow enhancements into one scalable platform.