Researchers at the National Institutes of Health and the University of Wisconsin have demonstrated that using artificial intelligence to analyze CT scans can produce more accurate risk assessment for major cardiovascular events than current, standard methods such as the Framingham risk score (FRS) and body-mass index (BMI).
More than 80 million body CT scans are performed every year in the U.S. alone, but valuable prognostic information on body composition is typically overlooked. In this study, for example, abdominal scans done for routine colorectal cancer screening revealed important information about heart-related risks – when AI was used to analyze the images.
The study compared the ability of automated CT-based body composition biomarkers derived from image-processing algorithms to predict major cardiovascular events and overall survival against routinely used clinical parameters. The investigators found that the CT-based measures were more accurate than FRS and BMI in predicting downstream adverse events including death or myocardial infarction, cerebrovascular accident, or congestive heart failure. The results appeared in The Lancet Digital Health.
“We found that automated measures provided more accurate risk assessments than established clinical biomarkers,” said Ronald M. Summers, M.D., Ph.D., of the NIH Clinical Center and senior author of the study. “This demonstrates the potential of an approach that uses AI to tap into the biometric data embedded in all such scans performed for a wide range of other indications and derive information that can help people better understand their overall health and risks of serious adverse events.”