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

Author

Listed:
  • Robert J. H. Miller

    (Imaging and Biomedical Sciences Cedars-Sinai Medical Center
    University of Calgary)

  • 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
    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
    University of Zielona Gora)

  • 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)

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.

Suggested Citation

  • 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. Krie, 2024. "Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46977-3
    DOI: 10.1038/s41467-024-46977-3
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    References listed on IDEAS

    as
    1. Roman Zeleznik & Borek Foldyna & Parastou Eslami & Jakob Weiss & Ivanov Alexander & Jana Taron & Chintan Parmar & Raza M. Alvi & Dahlia Banerji & Mio Uno & Yasuka Kikuchi & Julia Karady & Lili Zhang &, 2021. "Deep convolutional neural networks to predict cardiovascular risk from computed tomography," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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