EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation
Matin Daghyani, Lyuyang Wang, Nima Hashemi, Bassant Medhat, Baraa Abdelsamad, Eros Rojas Velez, XiaoXiao Li, Michael Y. C. Tsang, Christina Luong, Teresa S.M. Tsang, Purang Abolmaesumi

TL;DR
EchoAgent introduces a novel framework that combines vision tools and large language models to perform guideline-based, interpretable, and automated analysis of echocardiography videos, enhancing transparency and clinical relevance.
Contribution
The paper presents EchoAgent, a new system that integrates specialized vision tools with LLMs for structured, guideline-centric echocardiographic interpretation, including a measurement-feasibility prediction model.
Findings
Achieves accurate and interpretable echocardiographic analysis
Supports transparency through visual evidence grounding
Demonstrates full video-level automation in clinical settings
Abstract
Purpose: Echocardiographic interpretation requires video-level reasoning and guideline-based measurement analysis, which current deep learning models for cardiac ultrasound do not support. We present EchoAgent, a framework that enables structured, interpretable automation for this domain. Methods: EchoAgent orchestrates specialized vision tools under Large Language Model (LLM) control to perform temporal localization, spatial measurement, and clinical interpretation. A key contribution is a measurement-feasibility prediction model that determines whether anatomical structures are reliably measurable in each frame, enabling autonomous tool selection. We curated a benchmark of diverse, clinically validated video-query pairs for evaluation. Results: EchoAgent achieves accurate, interpretable results despite added complexity of spatiotemporal video analysis. Outputs are grounded in visual…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Domain Adaptation and Few-Shot Learning · Artificial Intelligence in Healthcare and Education
