Abstract
INTRODUCTION: About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied to CAC scans for opportunistic measurement of bone mineral density, cardiac chamber volumes, left ventricular mass, and other imaging biomarkers collectively referred to as AI-cardiovascular disease (CVD). In this study, we investigate a new AI-CVD algorithm for opportunistic measurement of liver steatosis. METHODS: We applied AI-CVD to CAC scans from 5702 asymptomatic individuals (52% female, age 62±10 years) in the Multi-Ethnic Study of Atherosclerosis. Liver attenuation index (LAI) was measured using the percentage of voxels below 40 Hounsfield units. We used Cox proportional hazards regression to examine the association of LAI with incident CVD and mortality over 15 years, adjusted for CVD risk factors and the Agatston CAC score. RESULTS: A total of 751 CVD and 1343 deaths accrued over 15 years. Mean±SD LAI in females and males was 38±15% and 43±13%, respectively. Participants in the highest versus lowest quartile of LAI had greater incidence of CVD over 15 years: 19% (95% CI 17% to 22%) vs 12% (10% to 14%), respectively, p<0.0001. Individuals in the highest quartile of LAI (Q4) had a higher risk of CVD (HR 1.43, 95% CI 1.08 to 1.89), stroke (HR 1.77, 95% CI 1.09 to 2.88), and all-cause mortality (HR 1.36, 95% CI 1.10 to 1.67) compared with those in the lowest quartile (Q1), independent of CVD risk factors. CONCLUSION: AI-enabled liver steatosis measurement in CAC scans provides opportunistic and actionable information for early detection of individuals at elevated risk of CVD events and mortality, without additional radiation.