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Cardiovascular diseases, a leading cause of global mortality, underscore the urgency for refined diagnostic techniques. Among these, cardiomyopathies characterized by abnormal heart wall thickening present a formidable challenge, exacerbated by aging populations and the side effects of chemotherapy. Traditional echocardiogram analysis, demanding considerable time and expertise, now faces overwhelming pressure due to escalating demands for cardiac care. This study addresses these challenges by harnessing the potential of Convolutional Neural Networks, specifically YOLOv8, U-Net, and Attention U-Net, leveraging the EchoNet-Dynamic dataset from Stanford University Hospital to segment echocardiographic images. Our investigation aimed to optimize and compare these models for segmenting the left ventricle in echocardiography images, a crucial step for quantifying key cardiac parameters. We demonstrate the superiority of U-Net and Attention U-Net over YOLOv8, with Attention U-Net achieving the highest Dice Coefficient Score due to its focus on relevant features via attention mechanisms. This finding highlights the importance of model specificity in medical image segmentation and points to attention mechanisms. The integration of AI in echocardiography represents a pivotal shift toward precision medicine, improving diagnostic accuracy and operational efficiency. Our results advocate for the continued development and application of AI-driven models, underscoring their potential to transform cardiovascular diagnostics through enhanced precision and multimodal data integration. This study validates the effectiveness of state-of-the-art AI models in cardiac function assessment and paves the way for their implementation in clinical settings, thereby contributing significantly to the advancement of cardiac healthcare delivery.
Kim et al. (Wed,) studied this question.