Artificial Intelligence-Enabled ECG Screening for LVSD in LBBB: Evaluating Model Development and Transfer Learning
Hak Seung Lee1, Sooyeon Lee2, Sora Kang1
1Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea; Medical AI Co, Ltd, Seoul, Republic of Korea.
JACC. Advances
|August 22, 2025
Summary
A general artificial intelligence (AI)-ECG model effectively screens for left ventricular systolic dysfunction (LVSD) in patients with left bundle branch block (LBBB). Transfer learning provides a small performance boost without needing curated LBBB data.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Left bundle branch block (LBBB) is a common ECG finding linked to left ventricular systolic dysfunction (LVSD).
- AI-ECG analysis shows potential for LVSD screening, but optimal model type for LBBB patients is unclear.
Purpose of the Study:
- To evaluate four AI-ECG models for LVSD detection in LBBB patients.
- To assess the impact of different training data strategies on model performance.
Main Methods:
- Developed four AI-ECG models: general, LBBB-specific (automated/expert), and transfer learning.
- Trained on over 364,000 ECGs; externally validated on 1,334 LBBB patient ECGs.
- Assessed performance using AUROC, sensitivity, and specificity.
Main Results:
- Transfer learning (AUROC 0.903) and general AI-ECG models (AUROC 0.899) showed comparable, high performance.
- LBBB-specific models had lower AUROCs (0.879, 0.841).
- General model excelled in sensitivity; transfer learning model in specificity.
Conclusions:
- A broad AI-ECG model is reliable for LVSD detection in LBBB patients.
- Transfer learning offers marginal improvement without specialized LBBB datasets.
- Validation in diverse clinical populations is crucial for AI-ECG tools.
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