Diagnostic Imaging's Weekly Scan: August 20-August 26

New research suggests the capability of a mammography-based deep learning model to identify women at high risk of breast cancer led to more than triple the cancer detection rate on breast MRI in comparison to traditional risk assessment tools.

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.

The artificial intelligence (AI)-enabled AIRAscore can reportedly provide quantitative brain volume data within five minutes of assessing brain MRI scans.

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.

Over 80 percent of head and neck computed tomography angiography (CTA) exams performed in the emergency department had no actionable findings for patients with non-focal neurologic complaints, and 67 percent of the exams were ordered by non-physician practitioners (NPPs), according to a newly published study.

A robotic guidance and placement system, the IGAR system can be utilized inside of a magnetic resonance imaging (MRI) bore.