Abstract Body (Do not enter title and authors here): Background
Asymptomatic left ventricular systolic dysfunction (LVSD) often precedes overt heart failure but frequently goes undetected in the general population. With the rise of artificial intelligence-enabled electrocardiogram (AI-ECG) now offer a scalable approach for early detection.

Hypothesis
We hypothesize that AI-ECG is capable of identifying LVSD in apparently healthy individuals undergoing routine check-ups.

Methods
This retrospective single-center study evaluated the performance of an AI-ECG model (AiTiALVSD) among 40,713 asymptomatic adults who underwent 60,711 paired ECG and echocardiography exams as part of self-referred health screenings from 2011 to 2023. LVSD was defined as left ventricular ejection fraction ≤40%. (Figure 1) Model performance was compared against two conventional heart failure risk scores.

Results
The AiTiALVSD model demonstrated excellent diagnostic accuracy with an area under the receiver operating characteristic curve of 0.973 and a precision-recall area of 0.328. (Figure 2) At the prespecified threshold, the model achieved 90.6% sensitivity, 99.4% specificity, 7.7% positive predictive value, and 100% negative predictive value. In comparison, conventional models performed worse (area under the curve of 0.696 for MESA and 0.672 for PCP-HF). (Figure 3) Simulation suggested that 1,841 ECGs and 13 echocardiograms would be required to identify one LVSD case. Notably, most false positives exhibited other echocardiographic abnormalities.

Conclusions
The AI-ECG model showed high diagnostic performance in a real-world low-prevalence setting, supporting its potential as a cost-effective, noninvasive screening tool for early detection of LVSD. These findings warrant prospective validation and suggest a promising role for AI in community-based heart failure prevention strategies.