Multi-View Echocardiographic Embedding for Accessible AI Development.

ABSTRACT:

Background and aims

Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized imaging views. While recent AI foundation models demonstrate superior capabilities across cardiac imaging tasks, their massive computational requirements and reliance on large-scale datasets create accessibility barriers, limiting AI development to well-resourced institutions. Vector embedding approaches offer promising solutions by leveraging compact representations from original medical images for downstream applications. Furthermore, demographic fairness remains critical, as AI models may incorporate biases that confound clinically relevant features. We developed a multi-view encoder framework to address computational accessibility while investigating demograp

SUBMITTER: Tohyama T 

PROVIDER: S-EPMC12393585 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature