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. DouglasUdo Hoffmann, Michael T. Lu, Hugo J. W. L. Aerts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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. 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.

Original languageEnglish
Article number715
Number of pages9
JournalNature Communications
Volume12
Issue number1
DOIs
Publication statusPublished - 29 Jan 2021

Keywords

  • CORONARY-ARTERY CALCIUM
  • CT ANGIOGRAPHY
  • CHEST-PAIN
  • CARDIAC CT
  • HEART
  • LUNG
  • CALCIFICATION
  • DISEASE
  • DESIGN
  • QUANTIFICATION

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