Using Artificial Intelligence to Predict CV Risk Assessment • CMHC PULSE

It is well-established that patients with the highest proportion of visceral fat area are more likely to experience a heart attack or other cardiovascular event. While abdominal CT scans can provide a more granular look at body composition when routinely performed, ascertaining risk levels based on fat area is rarely done in clinical practice. Manually obtaining measurements can be time intensive and costly, yet a single axial CT slice of the abdomen can visualize the volume of subcutaneous and visceral fat area as well as skeletal muscle area needed to predict the risk of major cardiovascular (CV) events. Used in combination with artificial intelligence (AI), CT imaging has the potential to offer an improved way of predicting adverse CV according to emerging research. As part of a retrospective study including over 23,000 patients, a team of researchers at Brigham and Women's Hospital in Boston analyzed over 33,000 CT scans performed.