Nature Communications (Jan 2021)
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,
- Hugo J. W. L. Aerts
Affiliations
- 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
- Department of Mathematics and Statistics, Boston University
- Taylor F. Mahoney
- Department of Biostatistics, Boston University School of Public Health
- Joseph M. Massaro
- Department of Biostatistics, Boston University School of Public Health
- Ramachandran S. Vasan
- National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study
- Pamela S. Douglas
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, 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
- DOI
- https://doi.org/10.1038/s41467-021-20966-2
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 9
Abstract
Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
