Ejection fraction quantification from ungated chest CT by AI
Jianhang Zhou1, Jacek Kwieciński1,2, Aakash Shanbhag1,3
1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Abstract:
Left ventricular ejection fraction (LVEF) is an important clinical metric, obtained by specialized imaging across the cardiac cycle. We present a novel AI approach to estimate LVEF from ungated chest CT. Using multicenter (11 sites) registry of 25,852 patients, AI-derived CT LVEF (AI LVEF) showed strong correlation with 3D gated positron emission tomography (r=0.84), area under the curve (AUC) of 0.96, negative predictive value of 95% for reduced LVEF (< 40%), and effectively stratified risk of heart failure, cardiovascular death, and all-cause death. In a separate large multicenter population (n=24,054) with lung CT scans, reduced AI LVEF was associated with a hazard ratio of 13.3 (95% confidence interval 9.7-18.4) for cardiovascular death. AI LVEF could also predict reduced echocardiographic LVEF (AUC=0.91). LVEF can be accurately estimated from non-contrast, ungated, low-dose chest CT scans, effectively stratifying patients for heart failure and mortality, with potential widespread clinical utility.
