# Artificial Intelligence–Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population

## TL;DR

An AI model using ECGs can accurately detect early heart issues in the general population, potentially preventing heart failure.

## Contribution

An AI model (AiTiALVSD) achieves high diagnostic accuracy for asymptomatic left ventricular systolic dysfunction in a large screening population.

## Key findings

- The AiTiALVSD model achieved an AUROC of 0.973 and a sensitivity of 90.6% for detecting LVSD.
- The model outperformed existing HF risk scores like MESA and Pooled Cohort Equations in discrimination metrics.
- Simulation suggested 1,841 ECGs and 13 TTEs would be needed to detect one case of LVSD in low-prevalence populations.

## Abstract

Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remains undiagnosed in the general population. Artificial intelligence–enabled electrocardiogram (ECG) analysis offers a scalable approach for early detection.

The purpose of this study was to evaluate the diagnostic performance of an artificial intelligence–enabled ECG model (AiTiALVSD) for identifying asymptomatic LVSD in a large health screening population.

In this retrospective, single-center study, we evaluated the AiTiALVSD model among 40,713 self-referred adults who underwent a total of 60,711 ECG-transthoracic echocardiography (TTE) pairs between 2011 and 2023. LVSD was defined as a left ventricular ejection fraction ≤40%. Model discrimination was assessed using the area under the receiver-operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC), and diagnostic performance metrics were compared with established HF risk scores.

Among 60,711 ECG–TTE pairs, 32 cases (0.054%) met the criteria for LVSD. The AiTiALVSD model demonstrated excellent discrimination (AUROC 0.973; AUPRC 0.328), with a sensitivity of 90.6%, specificity of 99.4%, positive predictive value of 7.7%, and a negative predictive value of 100%. Established HF risk scores, including the MESA (Multi-Ethnic Study of Atherosclerosis) 5-year HF score and Pooled Cohort Equations to Prevent HF score, showed inferior discrimination (AUROC: 0.696 and 0.672, respectively). The MESA score was not designed to detect prevalent LVSD and was calculated without natriuretic peptide data, which may have disadvantaged its performance in this comparison. Simulation analyses suggested that approximately 1,841 ECGs and 13 confirmatory TTEs would be required to detect one case of LVSD.

In a real-world screening population with an extremely low prevalence of LVSD, the AiTiALVSD model demonstrated high diagnostic accuracy, supporting its potential role as a rule-out screening tool for HF prevention. Prospective validation is warranted.