EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation

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 evidence and clinical guidelines, supporting transparency and traceability. Conclusion: This work demonstrates the feasibility of agentic, guideline-aligned reasoning for echocardiographic video analysis, enabled by task-specific tools and full video-level automation. EchoAgent sets a new direction for trustworthy AI in cardiac ultrasound.