Abstract

Deep learning models are increasingly applied to electrocardiogram (ECG) analysis to optimise cardiovascular care. However, potential biases within these models may impact their reliability and clinical applicability. This study investigates potential sex bias in deep learning models for 12-lead ECG classification of Sinus Rhythm (SR), Atrial Fibrillation (AF), and Myocardial Infarction (MI). We evaluate three model architectures—Convolutional Neural Network, xResNet101, and a Residual Network with an attention mechanism—under varying sex ratios in the training data. Among these models, the attention-based Residual Network demonstrated the highest and most equitable performance, particularly in SR and AF classification. MI classification exhibited pronounced sex-based disparities, even with balanced training data. These findings underscore the importance of incorporating fairness considerations in the development of clinical deep learning systems to ensure reliable and unbiased performance across diverse patient populations. Moreover, optimising lead selection may further enhance both fairness and overall model performance.