Related Experiment Video
Updated: Jan 18, 2026
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation
Do Heon Kim1, Youngnam Bok2, Sun Hwa Lee3,4
1Chungnam National University College of Medicine, Daejeon, Republic of Korea.
Insights
A new AI model fine-tuned on ECG data improves detection of left ventricular systolic dysfunction in patients with left bundle branch block. This AI tool offers a promising alternative when echocardiography is not immediately available, aiding early diagnosis and patient management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Left bundle branch block (LBBB) is a significant ECG finding associated with left ventricular systolic dysfunction (LVSD).
- Early detection of LVSD is critical for patient outcomes, but echocardiography is not always readily accessible.
- Standard diagnostic methods for LVSD can be time-consuming and may not be immediately available in all clinical settings.
Purpose of the Study:
- To develop and evaluate a fine-tuned ECG foundation model (FM) for enhanced LVSD detection in LBBB patients.
- To compare the performance of the proposed ECG-FM against conventional deep learning models.
- To identify key ECG features predictive of LVSD in LBBB patients using explainability techniques.
Main Methods:
- Retrospective multicenter analysis of 2,031 paired ECG-echocardiographic datasets from 892 LBBB patients.
- Fine-tuning a pre-existing ECG foundation model for LVSD prediction.
- Comparison with baseline deep learning models: Fully Convolutional Network (FCN), LSTM-FCN, ResNet, and InceptionTime.
- Utilized DeepLIFT analysis for feature importance assessment.
Main Results:
- The fine-tuned ECG-FM achieved an accuracy of 0.758, sensitivity of 0.771, and AUROC of 0.807, outperforming baseline models.
- Sequential partial fine-tuning yielded the highest sensitivity (0.787), enhancing screening capabilities.
- DeepLIFT analysis highlighted QRS complex and T wave features in leads V1-V4 as crucial predictive factors.
Conclusions:
- The fine-tuned ECG-FM significantly improves LVSD detection in LBBB patients.
- This AI-driven approach offers potential for earlier clinical diagnosis when echocardiography is unavailable.
- The model's ability to identify critical ECG features may enhance clinical management and improve patient outcomes.
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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