FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis
Xiaotian Hu, Junwei Huang, Mingxuan Liu, Kasidit Anmahapong, Yifei Chen, Yitong Luo, Yiming Huang, Xuguang Bai, Zihan Li, Yi Liao, Haibo Qu, Qiyuan Tian

TL;DR
FetalAgents introduces a multi-agent system that enhances fetal ultrasound analysis by coordinating specialized experts for diagnosis, measurement, segmentation, and video summarization, improving accuracy and workflow integration.
Contribution
This work presents the first multi-agent framework for comprehensive fetal US analysis, enabling dynamic coordination of vision experts for end-to-end clinical workflow support.
Findings
Outperforms specialized models and MLLMs across eight clinical tasks.
Supports end-to-end video stream summarization with keyframe identification.
Provides a robust, workflow-aligned solution for fetal ultrasound reporting.
Abstract
Fetal ultrasound (US) is the primary imaging modality for prenatal screening, yet its interpretation relies heavily on the expertise of the clinician. Despite advances in deep learning and foundation models, existing automated tools for fetal US analysis struggle to balance task-specific accuracy with the whole-process versatility required to support end-to-end clinical workflows. To address these limitations, we propose FetalAgents, the first multi-agent system for comprehensive fetal US analysis. Through a lightweight, agentic coordination framework, FetalAgents dynamically orchestrates specialized vision experts to maximize performance across diagnosis, measurement, and segmentation. Furthermore, FetalAgents advances beyond static image analysis by supporting end-to-end video stream summarization, where keyframes are automatically identified across multiple anatomical planes,…
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Taxonomy
TopicsFetal and Pediatric Neurological Disorders · Domain Adaptation and Few-Shot Learning · Teratomas and Epidermoid Cysts
