AI Determines Calcium Scores in CT and PET Images More Rapidly and Accurately than Physicians

Calcium scores found within the heart provide an accurate measure of atherosclerosis - a buildup of fats, cholesterol and other substances found in the artery walls that can lead to serious cardiac conditions. The assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. Coronary artery calcium is also visible in CT attenuation correction scans, always acquired with cardiac positron emission tomographic (PET) imaging. Now, a new study has revealed that artificial intelligence (AI) tools can more rapidly, and objectively, determine calcium scores in CT and PET images than physicians, even when obtained from very-low-radiation CT attenuation scans.

The novel deep learning model, originally developed for video applications, was adapted by researchers at Cedars-Sinai (Los Angeles, CA, USA) to rapidly quantify coronary artery calcium. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events. Same-day paired electrocardiographically gated CAC scans were available in 2,737 patients. The CT attenuation maps were obtained with PET/CT scans and could be processed by AI techniques for rapid and objective determination of coronary calcium score without additional scan and radiation. Using these AI and deep learning techniques requires less imaging, less radiation and lower costs, according to the researchers.

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