Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease

Joshua Mayourian, Addison Gearhart, William G. La Cava, Akhil Vaid, Girish N. Nadkarni, John K. Triedman, Andrew J. Powell, Rachel M. Wald, Anne Marie Valente, Tal Geva, Son Q. Duong, Sunil J. Ghelani

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Artificial intelligence–enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD). Objectives: The purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD. Methods: We trained (80%) and tested (20%) a convolutional neural network on paired ECG-CMRs (≤30 days apart) from patients with and without CHD to detect left ventricular (LV) dysfunction (ejection fraction ≤40%), RV dysfunction (ejection fraction ≤35%), and LV and RV dilation (end-diastolic volume z-score ≥4). Performance was assessed during internal testing and external validation on an outside health care system using area under receiver-operating curve (AUROC) and area under precision recall curve. Results: The internal and external cohorts comprised 8,584 ECG-CMR pairs (n = 4,941; median CMR age 20.7 years) and 909 ECG-CMR pairs (n = 746; median CMR age 25.4 years), respectively. Model performance was similar for internal testing (AUROC: LV dysfunction 0.87; LV dilation 0.86; RV dysfunction 0.88; RV dilation 0.81) and external validation (AUROC: LV dysfunction 0.89; LV dilation 0.83; RV dysfunction 0.82; RV dilation 0.80). Model performance was lowest in functionally single ventricle patients. Tetralogy of Fallot patients predicted to be at high risk of ventricular dysfunction had lower survival (P < 0.001). Model explainability via saliency mapping revealed that lateral precordial leads influence all outcome predictions, with high-risk features including QRS widening and T-wave inversions for RV dysfunction/dilation. Conclusions: AI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.

Original languageEnglish
Pages (from-to)815-828
Number of pages14
JournalJournal of the American College of Cardiology
Volume84
Issue number9
DOIs
StatePublished - 27 Aug 2024

Keywords

  • artificial intelligence
  • cardiovascular magnetic resonance
  • congenital heart disease
  • tetralogy of Fallot
  • ventricular function

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