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Purpose <p>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.</p> Methods <p>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. To assess robustness across institutions, we further evaluate EchoAgent on a curated subset of the publicly available MIMIC-IV-EchoQA benchmark, specifically targeting questions answerable via linear measurements to remain within the current framework’s scope.</p> Results <p>EchoAgent outperforms current medical VLMs and cardiac foundation models in video-level reasoning, demonstrating superior accuracy and interpretability on both our internal benchmark and the external MIMIC-IV-EchoQA subset. Outputs are grounded in visual evidence and clinical guidelines, supporting transparency and traceability.</p> Conclusion <p>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 provides a framework for enhancing transparency and guideline-adherence, representing a step toward more trustworthy AI in cardiac ultrasound. Our code will be made publicly available at <a href="https://github.com/DeepRCL/EchoAgent">https://github.com/DeepRCL/EchoAgent</a>.</p>

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EchoAgent: guideline-centric reasoning agent for echocardiography measurement and interpretation

  • 期刊论文

  • 发表日期：

    2026年5月15日

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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,
  • Purang Abolmaesumi,
  • Teresa S. M. Tsang

摘要

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. To assess robustness across institutions, we further evaluate EchoAgent on a curated subset of the publicly available MIMIC-IV-EchoQA benchmark, specifically targeting questions answerable via linear measurements to remain within the current framework’s scope.

Results

EchoAgent outperforms current medical VLMs and cardiac foundation models in video-level reasoning, demonstrating superior accuracy and interpretability on both our internal benchmark and the external MIMIC-IV-EchoQA subset. 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 provides a framework for enhancing transparency and guideline-adherence, representing a step toward more trustworthy AI in cardiac ultrasound. Our code will be made publicly available at https://github.com/DeepRCL/EchoAgent.