Comprehensive echocardiogram evaluation with view primed vision language AI

"Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function1. Artificial intelligence (AI) in echocardiography has the potential to streamline manual tasks and improve reproducibility and precision2. However, most echocardiography AI models are single-view, single-task systems that do not synthesize complementary information from multiple views captured during a full exam3,4, and thus lead to limited performance and scope of applications."

"To address this problem, we introduce EchoPrime, a multi-view, view-informed, video-based vision-language foundation model trained on over 12 million video-report pairs. EchoPrime uses contrastive learning to train a unified embedding model for all standard views in a comprehensive echocardiogram study with representation of both rare and common diseases and diagnoses. EchoPrime then utilizes view-classification and a view-informed anatomic attention module to weight video-specific embeddings that accurately map the relationship between echocardiographic views and anatomical structures."

Echocardiography captures ultrasound video data to assess cardiac structure and function. Artificial intelligence in echocardiography can streamline manual tasks and improve reproducibility and precision. Most existing echocardiography AI models are single-view, single-task systems that fail to synthesize complementary information from multiple views, limiting performance and application scope. EchoPrime is a multi-view, view-informed, video-based vision-language foundation model trained on over 12 million video-report pairs. EchoPrime employs contrastive learning to create a unified embedding for all standard views and represents both rare and common diseases. EchoPrime uses view-classification and a view-informed anatomic attention module to weight video-specific embeddings and supports retrieval-augmented, holistic clinical interpretation across full studies.

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