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Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification

Arunashis Sau1,2, Henry Zhang1, Joseph Barker1

  • 1National Heart and Lung Institute, Imperial College London, London, United Kingdom.

JAMA Cardiology
|August 20, 2025
PubMed

Insights

Artificial intelligence-enhanced electrocardiography (AI-ECG) can predict complete heart block (CHB) risk. This AI-ECG tool, AIRE-CHB, offers improved risk stratification compared to traditional methods for identifying patients at risk of CHB.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Complete heart block (CHB) is a serious condition with crude risk stratification using current electrocardiography (ECG).
  • Artificial intelligence-enhanced ECG (AI-ECG) shows promise in identifying subclinical diseases.
  • There is a need for improved methods to predict incident CHB.

Purpose of the Study:

  • To develop and validate an AI-ECG risk estimator for predicting incident CHB.
  • To assess the performance of the AI-ECG model against traditional risk factors.

Main Methods:

  • A cohort study involving development and external validation.
  • Utilized a residual convolutional neural network architecture with a discrete-time survival loss function.
  • Trained the AI-ECG model (AIRE-CHB) to predict new CHB diagnoses.

Main Results:

  • AIRE-CHB demonstrated strong predictive performance in both development (C-index 0.836, AUROC 0.889) and validation cohorts (C-index 0.936).
  • The AI-ECG model significantly outperformed traditional bifascicular block detection (AUROC 0.594).
  • High-risk individuals identified by AIRE-CHB had substantially increased hazard ratios for developing CHB.

Conclusions:

  • A novel deep learning model, AIRE-CHB, can effectively identify the risk of incident CHB.
  • AIRE-CHB has the potential to enhance clinical decision-making for patients with syncope or at risk of high-grade atrioventricular block.
  • This AI-ECG approach offers a significant advancement over current risk stratification methods for CHB.
Abstract

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