## 3 Results and Discussion
### Discussion  
Identification of LVSD has clinical significance because, even if asymptomatic, it is associated with increased mortality and improbitities. On the other hand, there is an opportunity to prevent the progression of LVSD with timely medical and/or device therapy [25; 26]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lead** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline I & 0.75 & 0.8 & 0.43 & 0.56 & 0.68 \\ II & 0.76 & 0.69 & 0.65 & 0.67 & 0.74 \\ III & 0.77 & 0.75 & 0.57 & 0.65 & 0.73 \\ aVR & 0.74 & 0.7 & 0.51 & 0.59 & 0.69 \\ aVL & 0.76 & 0.78 & 0.49 & 0.6 & 0.7 \\ aVF & 0.72 & 0.66 & 0.51 & 0.58 & 0.68 \\ V1 & 0.74 & 0.92 & 0.34 & 0.5 & 0.66 \\ V2 & 0.79 & 0.92 & 0.44 & 0.59 & 0.71 \\ V3 & 0.76 & 0.85 & 0.41 & 0.55 & 0.68 \\ V4 & 0.73 & 0.68 & 0.43 & 0.53 & 0.66 \\ V5 & 0.72 & 0.62 & 0.5 & 0.55 & 0.67 \\ V6 & 0.72 & 0.65 & 0.49 & 0.55 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Single lead performance metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental Cases** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline Existing Model & 0.6 & 0.62 & 0.2 & 0.28 & 0.56 \\ Single Lead (CNN Model) & 0.74 & 0.7 & 0.48 & 0.58 & 0.69 \\ Multi Lead (Lead-Wise Ensemble) & 0.81 & 0.87 & 0.56 & 0.68 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results. Conventional tools for assessing LVEF, including echocardiography, cardiac magnetic resonance imaging, and cardiac computed tomographic angiography, are resource-intensive and time-consuming when used for screening purposes in real-world clinical settings. In this context, recent AI models using the ECG, an inexpensive and readily available tool even in resource-limited environments, have attracted great attention, showing high performance in LVSD detection. LBBB is well known to be associated with LVSD, whether it is the cause or consequence of early cardiac structural changes. However, pre-existing models have not been validated for patients with LBBB. As shown in the Section 3.1, an existing open-source algorithm [12] did not perform well with our LBBB data. Thus, the development of advanced AI models is necessary to detect LVSD in the LBBB population. This pilot study focused on exploring the feasibility of an AI-enabled ECG approach rather than developing a high-performance model, considering the small dataset obtained from a single institution. Previous studies on ECG-based heart disease prediction primarily utilized a single CNN model or methods that combined CNN with other AI techniques such as U-Net, RNN, and LSTM models, demonstrating relatively high performance. However, considering the limitations of the dataset size in this study, the proposed approach that combines a CNN model with an autoencoder-based anomaly detection model outperformed these conventional methods. Although there are limitations in model performance and dataset size, the findings of this study suggest the potential applicability of deep learning techniques in detecting LVSD among patients with LBBB. There would be a merit to apply an AI algorithm for LBBB detection to enhance the efficiency of LVSD prediction model even in non-LBBB subjects. Even though we used an LVEF \(\leq\) 35%, a fine-tuned AI model for detecting structural and/or functional abnormality in the early phase, including mild reduction in LVEF, will carry more clinical relevance as it can allow timely intervention. However, certain limitations must be acknowledged. The small dataset size may have impacted the model's generalizability, and further validation with larger, more diverse datasets is necessary. Additionally, due to the small sample size, we used a single train-test split to ensure strict separation between training and validation sets; however, we acknowledge that k-fold cross-validation or bootstrapping could provide more stable and generalizable estimates, and we plan to adopt such methodologies in future studies.