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Artificial intelligence-based accurate myocardial infarction mapping using 12-lead electrocardiography.

Hui Wang1, Zhifan Gao2, Heye Zhang2

  • 1Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.

European Heart Journal. Digital Health
|September 23, 2025
PubMed
Summary

Artificial intelligence-assisted electrocardiography (AI-ECG) can detect myocardial fibrosis (MF) after myocardial infarction (MI). A novel AI-MI-12ECG method, trained with biosimulation, accurately identifies MF location and size, aligning with cardiac magnetic resonance imaging (CMR).

Keywords:
12-Lead electrocardiographyArtificial intelligenceBiosimulationLate gadolinium-enhancedMyocardial fibrosis

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Assessing myocardial fibrosis (MF) post-myocardial infarction (MI) is critical for patient prognosis.
  • Artificial intelligence-assisted electrocardiography (AI-ECG) shows promise for detecting MF.
  • Training AI-ECG models requires extensive data; biosimulation offers a potential solution.

Purpose of the Study:

  • To develop and validate a novel AI-assisted method, AI-MI-12ECG, for assessing MF using 12-lead ECG.
  • To utilize a biosimulation model for training the AI-MI-12ECG system.
  • To evaluate the accuracy of AI-MI-12ECG in detecting MF presence, location, and size in post-MI patients.

Main Methods:

  • A biosimulation model was used to train the AI-MI-12ECG system.
  • 182 post-MI patients were enrolled in a prospective study.
  • AI-MI-12ECG findings were compared against late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) imaging.

Main Results:

  • AI-MI-12ECG demonstrated a strong correlation (R=0.955) with CMR-LGE in identifying MI location.
  • The method achieved high accuracy in detecting fibrosis across coronary artery territories: LAD (0.95), RCA (0.97), and LCX (0.91).
  • Receiver operating characteristic curves showed excellent performance for AI-MI-12ECG: 0.95 (LAD), 0.95 (RCA), and 0.89 (LCX).

Conclusions:

  • The AI-MI-12ECG method, trained via biosimulation, aligns well with CMR-LGE in post-MI patients.
  • This AI-ECG approach shows significant potential for accurate myocardial fibrosis detection.
  • AI-MI-12ECG can effectively identify patients with substantial infarct burdens, aiding in prognosis.