AI predicts cardiovascular disease mortality risk on chest CT

A deep-learning algorithm could predict a patient’s risk of atherosclerotic cardiovascular disease (ASCVD) from analysis of noncontrast-enhanced chest CT exams, according to a study presented at the recent RSNA meeting.  Researchers, led by presenter Vineet Kalathur Raghu, PhD, from Harvard Medical School and Massachusetts General Hospital, trained an AI model to predict a patient’s probability of cardiovascular mortality within 12 years. In testing, the algorithm yielded a statistically significant improvement in prediction performance over a baseline regression model. The algorithm, called CT-CV-Risk, also predicted cardiovascular mortality beyond CAC and baseline risk factors, according to the researchers. “We hope that this can help improve cardiovascular risk stratification to guide primary prevention,” Raghu told session attendees.Coronary artery calcium (CAC) scoring on CT exams can be utilized to estimate the 10-year risk of ASCVD. In 2018, the American Heart Association and American College of Cardiology published cholesterol guidelines that recommend CAC scoring be used to guide decisions on whether adults with at an intermediate (7.5% to 20%) 10-year risk should take a statin.Raghu's group, as well as other researchers, have previously demonstrated that convolutional neural networks (CNNs) can accurately measure CAC on noncontrast-enhanced chest CT scans, including low-dose lung cancer screening CT exams.“This automated CAC score is highly correlated t...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: CT Artificial Intelligence Image Processing Chest Radiology Source Type: news