Abstract
Left bundle branch block (LBBB) is an important electrocardiographic (ECG) finding strongly associated with left ventricular systolic dysfunction (LVSD), a condition linked to poor clinical outcomes. Although early LVSD detection is crucial, standard diagnosis via echocardiography may not always be immediately accessible. In this study, we propose a fine-tuned ECG foundation model (FM) to enhance LVSD detection specifically in patients with LBBB. We conducted a retrospective multicenter analysis of 2,031 paired ECG-echocardiographic datasets from 892 LBBB patients. The ECG-FM was fine-tuned for optimal LVSD prediction and compared against baseline models, which were conventional deep learning methods, including Fully Convolutional Network (FCN), LSTM-FCN, ResNet, and InceptionTime. The proposed ECG-FM with single-step full fine-tuning outperformed baseline models, achieving accuracy, sensitivity, and AUROC of 0.758, 0.771, and 0.807, respectively. Additionally, sequential partial fine-tuning exhibited the highest sensitivity (0.787), enhancing screening capability. DeepLIFT analysis identified QRS complex and T wave features in leads V1–V4 as critical predictive factors. Our results demonstrated that the recommended fine-tuned ECG-FM significantly improves LBBB patient LVSD detection, potentially enabling earlier clinical diagnosis in such cases when echocardiography is not readily available, thereby potentially improving patient outcomes and clinical management.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Due to the retrospective nature of the study, (Chungnam National University Hospital IRB 2025-02-001 and Jeonbuk National University Hospital IRB 2025-02-030) waived the need of obtaining informed consent.
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Funding
This work was supported by Chungnam National University Research Fund, 2023-0540-01 and by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00258971, RS-2021-II211343, Artificial Intelligence Graduate School Program (Seoul National University)].
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Study concept/design: J-H.P., D.L., Data collection: Y.B., S.H.L., Data processing: S.P, D.H.K, J.H.P, Data analysis and interpretation: D.H.K, D.L., J-H.P., Manuscript preparation: D.H.K., Manuscript editing: D.L., J-H.P., Final approval of this version to be submitted: all authors.
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Kim, D.H., Bok, Y., Lee, S.H. et al. Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34911-6
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DOI: https://doi.org/10.1038/s41598-025-34911-6
