Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.
Compared to standard hip-to-waist ratio measurements and BMI, the algorithm identifies significantly more instances of metabolic syndrome and its severity in individual patients.
Accelerated MRI with AI image reconstruction nearly halved orthopedic scan times while maintaining or even improving image quality in a newly published prospective study.
When trained with high-fidelity simulation, junior radiology residents can master the discipline of reading whole-body CTs right at the trauma scanner—and doing so with high diagnostic accuracy, work speed and interpretive confidence.
Researchers at the Hospital for Special Surgery in New York City have demonstrated fast but fine 3D lumbar image acquisition on MRI using deep learning image reconstruction.
When resident teams included experienced fourth-year trainees, the resident/attending pairs cut overall median report turnaround times by seven minutes versus attending-only efforts.
A population-level study featuring multi-organ MRI has confirmed that problems in any of three major organs—the heart, brain or liver—tend to co-occur with unfavorable findings in either or both of the other two.
Prior to the final proposal’s release, the American College of Radiology reached out to CMS to offer its recommendations on payment rates for five out of the six the new codes.
