EchoPrime: A Multi-Video View-Informed Vision-Language Model for
  Comprehensive Echocardiography Interpretation
Authors: name: Milos Vukadinovic, name: Xiu Tang, name: Neal Yuan, name: Paul Cheng, name: Debiao Li, name: Susan Cheng, name: Bryan He, name: David Ouyang

Echocardiography is the most widely used cardiac imaging modality, capturing
ultrasound video data to assess cardiac structure and function. Artificial
intelligence (AI) in echocardiography has the potential to streamline manual
tasks and improve reproducibility and precision. However, most echocardiography
AI models are single-view, single-task systems that do not synthesize
complementary information from multiple views captured during a full exam, 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 model to weight
video-specific interpretations that accurately maps the relationship between
echocardiographic views and anatomical structures. With retrieval-augmented
interpretation, EchoPrime integrates information from all echocardiogram videos
in a comprehensive study and performs holistic comprehensive clinical
echocardiography interpretation. In datasets from two independent healthcare
systems, EchoPrime achieves state-of-the art performance on 23 diverse
benchmarks of cardiac form and function, surpassing the performance of both
task-specific approaches and prior foundation models. Following rigorous
clinical evaluation, EchoPrime can assist physicians in the automated
preliminary assessment of comprehensive echocardiography.