Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Journal: Circulation. Cardiovascular quality and outcomes
PMID: 39221857

Abstract

BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to ECG images as a surrogate for imaging risk biomarkers and its association with early CTRCD.

Authors

  • Evangelos K Oikonomou
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK.
  • Veer Sangha
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Lovedeep S Dhingra
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Arya Aminorroaya
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Andreas Coppi
  • Harlan M Krumholz
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Lauren A Baldassarre
    Section of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

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