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

Area of Science:

• Artificial Intelligence in Medical Imaging
• Cardiovascular Ultrasound Analysis
• Deep Learning for Healthcare

Background:

• Current deep learning models lack video-level reasoning for echocardiography.
• Echocardiographic interpretation necessitates complex analysis of video data and guideline adherence.
• Existing AI struggles with structured, interpretable automation in cardiac ultrasound.

Purpose of the Study:

• To introduce EchoAgent, a novel framework for automated echocardiographic video interpretation.
• To enable structured, interpretable, and guideline-based analysis of cardiac ultrasound videos.
• To address the limitations of current AI in video-level reasoning for echocardiography.

Main Methods:

• EchoAgent utilizes a large language model (LLM) to control specialized vision tools for temporal localization, spatial measurement, and interpretation.
• A key innovation is a measurement-feasibility prediction model for autonomous tool selection.
• The framework was evaluated on a curated benchmark of video-query pairs and the MIMIC-IV-EchoQA dataset.

Main Results:

• EchoAgent demonstrated superior accuracy and interpretability compared to existing medical vision-language models (VLMs) and cardiac foundation models.
• Performance was validated on both internal benchmarks and an external dataset (MIMIC-IV-EchoQA).
• The AI's outputs are grounded in visual evidence and clinical guidelines, ensuring transparency.

Conclusions:

• This study confirms the feasibility of agentic, guideline-aligned reasoning for echocardiographic video analysis.
• EchoAgent offers a framework for improved transparency and adherence to clinical guidelines in AI-driven cardiac ultrasound.
• The development represents a significant advancement toward trustworthy AI in cardiovascular diagnostics.