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arXiv · 2511.13948

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
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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.

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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. 2025-11-17. EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation. https://arxiv.org/abs/2511.13948

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