Podcast: A Conversation about Lowering Radiation Dose

Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.

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.

Looking for an enlightened approach to billing isn’t easy amid a sea of third-party payor excuses, absurd addendum requests and CT headers seemingly etched in stone.

In a new podcast, Mahadevappa Mahesh, MS, Ph.D., and Joseph Cavallo, M.D., share their perspectives on recently published research looking at projections for future radiation-induced cancers from computed tomography (CT) scans.

Researchers also noted a greater than 30 percent increase in treatment management changes resulting from the use of CT-based adjunctive AI to detect lung metastases in colorectal cancer patients.

Emphasizing increasing recognition of the capability of coronary computed tomography angiography (CCTA) for the evaluation of acute and stable chest pain, this author defuses common misperceptions and reviews key considerations for implementation of a CCTA program.