## 1 Introduction  
An electrocardiogram (ECG) is a noninvasive test to assess the heart's electrical activity and provides information on cardiac arrhythmia and structural heart disease. A 12-lead ECG records the electrical activity from the body surface in 12 directions on the vertical and horizontal planes. Left bundle branch block (LBBB), which is detected on an ECG when intraventricular conduction via the left bundle is impeded, can be accompanied by left ventricular systolic dysfunction (LVSD) [1, 2].  
Generally, LVSD is represented by the left ventricular ejection fraction (LVEF), the fraction of blood volume ejected from the left ventricle in systole in relation to end-diastolic volume, which is assessed by echocardiography. Considering the adverse outcomes of LVSD, early recognition of LVSD is important but echocardiography is not readily accessible in real-world clinical settings, especially in asymptomatic individuals. The plasma B-type natriuretic peptide (BNP) level can be used for LVSD detection [3], but it is less useful unless accompanied by heart failure [4, 5]. It is also an invasive test requiring blood sampling, and there is no standardized cut-off value that can be applied across different populations. Compared to the ECG, which is easily repeatable and reproducible, echocardiography and BNP-level measurement require additional resources, leading to increased costs and time requirements. For this reason, research has been conducted to utilize the ECG as an alternative screening tool for LVSD.  
Since the 1990s, efforts have been made to utilize ECG for detecting LVSD [6, 7]. However, traditional ECG-based diagnostic criteria have limited sensitivity and specificity. With the advent of artificial intelligence (AI) and machine learning techniques, several studies have proposed AI-enabled ECG algorithms for LVSD screening and diagnosis [8, 9, 10], demonstrating improved diagnostic performance across diverse populations [11, 12, 13, 14]. In addition, research has been conducted in specific clinical subgroups, including intensive care unit patients [15], patients presenting to emergency departments [16], and individuals diagnosed with COVID-19 [17]. Recently, some studies have explored the application of Transformer-based architectures for ECG anomaly detection, showing promising performance in capturing temporal dependencies and handling limited datasets [18, 19]. However, most prior AI-ECG models, including these Transformer-based approaches, have not been specifically validated in patients with LBBB.  
Despite these advances, most prior studies have focused on general populations and have not been validated specifically in patients with LBBB. The presence of LBBB can alter ECG waveforms significantly, complicating the recognition of LVSD-related features and potentially limiting model performance. Therefore, there remains a critical gap in developing and validating AI models tailored for LVSD prediction in this clinically important subgroup.  
To address this gap, we propose a deep learning model designed to predict LVSD specifically in patients with LBBB. Our approach integrates practical strategies, including autoencoder-based pretraining for robust representation learning, a CNN-based classification model for detecting LVSD, and a lead-wise ensemble technique to leverage complementary spatial information from different ECG leads. A pilot experiment was conducted using a dataset from a single tertiary university hospital to evaluate the feasibility and effectiveness of the proposed method in a real-world clinical setting.