Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

Robert J. H. Miller, Aditya Killekar, Aakash Shanbhag, Bryan Bednarski, Anna M. Michalowska, Terrence D. Ruddy, Andrew J. Einstein, David E. Newby, Mark Lemley, Konrad Pieszko, Serge D. Kriekinge, Paul B. Kavanagh, Joanna X. Liang, Cathleen Huang, Damini Dey, Daniel S. Berman and Piotr J. Slomka ()
Additional contact information
Robert J. H. Miller: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Aditya Killekar: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Aakash Shanbhag: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Bryan Bednarski: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Anna M. Michalowska: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Terrence D. Ruddy: University of Ottawa Heart Institute, Ottawa
Andrew J. Einstein: Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York
David E. Newby: University of Edinburgh
Mark Lemley: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Konrad Pieszko: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Serge D. Kriekinge: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Paul B. Kavanagh: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Joanna X. Liang: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Cathleen Huang: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Damini Dey: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Daniel S. Berman: Imaging and Biomedical Sciences Cedars-Sinai Medical Center
Piotr J. Slomka: Imaging and Biomedical Sciences Cedars-Sinai Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Date: 2024
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DOI: 10.1038/s41467-024-46977-3

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