Deep convolutional neural networks to predict cardiovascular risk from computed tomography

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, Jan-Erik Scholtz, Thomas Mayrhofer, Asya Lyass, Taylor F. Mahoney, Joseph M. Massaro, Ramachandran S. Vasan, Pamela S. Douglas, Udo Hoffmann, Michael T. Lu and Hugo J. W. L. Aerts ()
Additional contact information
Roman Zeleznik: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
Borek Foldyna: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, 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
Ivanov Alexander: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
Jana Taron: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
Chintan Parmar: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, 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
Julia Karady: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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
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
Pamela S. Douglas: Duke Clinical Research Institute
Udo Hoffmann: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
Michael T. Lu: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School
Hugo J. W. L. Aerts: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-20966-2

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