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Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in

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Artificial intelligence-enabled ECG analysis can detect asymptomatic left ventricular systolic dysfunction (LVSD), a precursor to heart failure (HF). This AI model shows high accuracy, suggesting its potential as a screening tool for HF prevention.

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Preventive Cardiology

Background:

  • Asymptomatic left ventricular systolic dysfunction (LVSD) precedes overt heart failure (HF) but is often undiagnosed.
  • Artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis presents a scalable method for early detection of LVSD.

Purpose of the Study:

  • To assess the diagnostic performance of the AiTiALVSD AI-enabled ECG model.
  • To identify asymptomatic LVSD within a large health screening cohort.

Main Methods:

  • Retrospective, single-center evaluation of the AiTiALVSD model using 60,711 ECG-transthoracic echocardiography (TTE) pairs from 40,713 adults (2011-2023).
  • LVSD defined as left ventricular ejection fraction ≤40%.
  • Model discrimination assessed via AUROC and AUPRC; performance compared to established HF risk scores.

Main Results:

  • The AiTiALVSD model demonstrated excellent discrimination (AUROC 0.973, AUPRC 0.328) with high sensitivity (90.6%) and specificity (99.4%).
  • The model achieved a negative predictive value of 100%, indicating strong potential for ruling out LVSD.
  • AiTiALVSD outperformed traditional HF risk scores (MESA, Pooled Cohort Equations) in discrimination.

Conclusions:

  • The AiTiALVSD model exhibits high diagnostic accuracy for asymptomatic LVSD in a low-prevalence screening population.
  • The AI model shows promise as a rule-out screening tool for heart failure prevention.
  • Further prospective validation of the AiTiALVSD model is recommended.