The fine-tuned ECG foundation model significantly improves detection of LVSD in LBBB patients compared to traditional ECG analysis.
Observational (n=892)
Yes
Does a fine-tuned ECG foundation model improve the prediction of left ventricular systolic dysfunction in patients with left bundle branch block compared to conventional deep learning models?
A fine-tuned ECG foundation model significantly improves the detection of left ventricular systolic dysfunction in patients with LBBB compared to conventional deep learning models, potentially enabling earlier screening when echocardiography is not readily available.
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.
Kim et al. (Sat,) conducted a observational in Left Ventricular Systolic Dysfunction (n=892). Fine-tuned ECG Foundation Model vs. Conventional deep learning methods (FCN, LSTM-FCN, ResNet, InceptionTime) was evaluated on Detection of Left Ventricular Systolic Dysfunction defined as LVEF < 40%. The fine-tuned ECG foundation model significantly improves detection of LVSD in LBBB patients compared to traditional ECG analysis.