Abstract Body (Do not enter title and authors here): Background
Left bundle branch block (LBBB) is a common ECG abnormality linked to left ventricular systolic dysfunction (LVSD), yet its altered conduction pattern complicates ECG-based diagnosis. While artificial-intelligence enabled ECG (AI-ECG) show promise for LVSD screening, it is unclear whether subgroup-specific training or transfer learning improves performance.

Hypothesis
We hypothesize that diagnostic performance for detecting LVSD in LBBB patients varies depending on the model development strategy, including training cohort selection and transfer learning approaches.

Methods
We developed four AI-ECG models using 364,845 ECGs: (1) a general model, (2) a model trained on automatically extracted LBBB cases, (3) a model trained on expert-validated LBBB ECGs, and (4) a transfer learning model fine-tuned on expert-curated LBBB data. All models were externally validated on 1,334 ECGs from LBBB patients at a separate hospital. LVSD was defined as ejection fraction ≤40%. Performance was assessed using AUROC, sensitivity, specificity, and predictive values.

Results
The transfer learning model achieved the highest AUROC (0.903; 95% CI: 0.887–0.918), followed by the general model (0.899; 95% CI: 0.883–0.915). Models trained solely on LBBB datasets performed worse (AUROC 0.879 and 0.841). The general model had the highest sensitivity (96.6%), while the transfer learning model had greater specificity (90.3%) and PPV (89.4%). (Figure 1) Kaplan–Meier analysis showed that AI-ECG false positives had significantly higher risk of future LVSD than true negatives (38% vs. 8.5%, p<0.0001). (Figure 2) We further explored ECG characteristics of LVSD in LBBB patients to evaluate model explainability. (Figure 3)

Conclusions
A general AI-ECG model effectively detects LVSD in LBBB patients, and transfer learning offers modest improvement. Model development strategies, especially training cohort composition, influence diagnostic performance in high-risk subgroups. AI-ECG holds promise for screening and prognostication in LBBB populations, warranting further validation.