{"ID":2840839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13948","arxiv_id":"2511.13948","title":"EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation","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.","short_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 orchestrate...","url_abs":"https://arxiv.org/abs/2511.13948","url_pdf":"https://arxiv.org/pdf/2511.13948v1","authors":"[\"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\"]","published":"2025-11-17T22:06:12Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}