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Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Author

Listed:
  • Roman Zeleznik

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Harvard Medical School)

  • Borek Foldyna

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Parastou Eslami

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Jakob Weiss

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Harvard Medical School
    Eberhard Karls University of Tübingen)

  • Ivanov Alexander

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Jana Taron

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Eberhard Karls University of Tübingen)

  • Chintan Parmar

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Harvard Medical School)

  • Raza M. Alvi

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Dahlia Banerji

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Mio Uno

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Yasuka Kikuchi

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Hokkaido University)

  • Julia Karady

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Semmelweis University)

  • Lili Zhang

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Jan-Erik Scholtz

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Thomas Mayrhofer

    (Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Stralsund University of Applied Sciences)

  • Asya Lyass

    (Boston University)

  • Taylor F. Mahoney

    (Boston University School of Public Health)

  • Joseph M. Massaro

    (Boston University School of Public Health)

  • Ramachandran S. Vasan

    (National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study
    Boston University School of Medicine)

  • Pamela S. Douglas

    (Duke Clinical Research Institute)

  • Udo Hoffmann

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Michael T. Lu

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School)

  • Hugo J. W. L. Aerts

    (Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
    Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
    Harvard Medical School
    Dana-Farber Cancer Institute, Harvard Medical School)

Abstract

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-20966-2
    DOI: 10.1038/s41467-021-20966-2
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    Cited by:

    1. Jakob Weiss & Vineet K. Raghu & Dennis Bontempi & David C. Christiani & Raymond H. Mak & Michael T. Lu & Hugo J.W.L. Aerts, 2023. "Deep learning to estimate lung disease mortality from chest radiographs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. 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.
    3. Anjan Gudigar & Sneha Nayak & Jyothi Samanth & U Raghavendra & Ashwal A J & Prabal Datta Barua & Md Nazmul Hasan & Edward J. Ciaccio & Ru-San Tan & U. Rajendra Acharya, 2021. "Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization," IJERPH, MDPI, vol. 18(19), pages 1-27, September.

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