March 17, 2020

Artificial intelligence predicts heart disease risk from CT scans

At a Glance

  • Artificial intelligence-based analysis of CT scans predicted people’s risk of heart disease more accurately than current methods.
  • The study shows the potential of using machine learning to extract additional information from imaging tests.
Patient undergoing CAT scan of the abdomen The study adds to previous findings that AI can be used to diagnose diseases and conditions by examining CT scans that were taken for other purposes. Monkeybusinessimages / iStock / Getty Images Plus

Cardiovascular disease remains the leading cause of death in the United States. If people at high risk of heart attack, stroke, and related conditions could be identified, medications and lifestyle changes may help reduce their risk of disease and death.

Traditionally, heart disease risk has been assessed using clinical measurements. These include body mass index (BMI)—a ratio of weight to height. Another commonly used measurement is the Framingham risk score (FRS), which incorporates age, sex, blood pressure, blood cholesterol, and related information. However, these tools are not precise. They can miss people at high risk and misidentify others who are not.

A research team led by Drs. Perry J. Pickhardt of the University of Wisconsin and Ronald Summers from the NIH Clinical Center has been developing computer programs to estimate disease risk from CT scans taken for other purposes. Tens of millions of people undergo such scans every year for reasons ranging from accidents to surgical planning.

The researchers previously showed that CT scans could be re-used to diagnose osteoporosis. In their new study, they tested whether artificial intelligence (AI) algorithms they’d developed to re-analyze CT scans could predict the risk of heart disease better than BMI or the FRS.

Schematic of aortic calcification in CT scan The AI made five body-composition measures based on CT scans, including calcification in the aortic artery, illustrated here.Pickhardt et al., Lancet Digital Health

The team used CT scans of the abdomen previously taken for colorectal cancer screening, from more than 9,200 men and women without symptomatic heart disease. Participants had an average age of 57. The AI programs measured calcification in the aortic artery, muscle density, the ratio of fat deep in the body to that under the skin, liver fat, and bone-mineral density as seen on the scans.

The researchers collected follow-up information for all participants for an average of almost 9 years. They then assessed whether their AI measures correlated with later development of heart disease or death. Results were published on March 2, 2020, in Lancet Digital Health.

Over the follow-up period, 20% of study participants experienced a heart attack or stroke, developed heart failure, or died. All five body-composition measures assessed by AI differed substantially between people who had and had not developed heart disease.

AI scores of calcification in the aortic artery alone were better than the FRS at predicting heart disease risk. All five measures alone were more predictive than BMI taken at the start of the study. In general, combining more than one of the AI measurements increased the ability to predict later heart disease risk from an abdominal scan. Adding the FRS to the AI measurements did not improve their predictive performance.

“We found that automated measures provided more accurate risk assessments than established clinical biomarkers,” Summers explains.

But because CT imaging comes with some risks, including exposure to small amounts of radiation, the researchers don’t propose taking CT scans solely for heart-disease risk assessment.

“This opportunistic use of additional CT-based biomarkers provides objective value to what doctors are already doing,” Pickhardt says. “This automated process requires no additional time, effort, or radiation exposure to patients.”

Related Links

References: Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Perry J Pickhardt, Peter M Graffy, Ryan Zea, Scott J Lee, Jiamin Liu, Veit Sandfort, Ronald M Summers. The Lancet Digital Health. 2 March 2020.

Funding: NIH’s Clinical Center (CC).