FetalAgents orchestrates specialized fetal-ultrasound models through multiple agents, achieving top performance across eight clinical tasks on external validation and auto-generating structured reports
The work proposes FetalAgents, a multi-agent system built on AutoGen in which a GPT-5-mini-driven Coordinator parses clinical intent and anatomical plane and dynamically dispatches expert agents that combine specialized vision models such as FetalCLIP, nnU-Net, USFM, SAMUS, and AoP-SAM via deterministic fusion rules, while a Summarizer consolidates outputs into structured reports; across eight clinical tasks (standard plane classification, brain plane classification, abdomen and stomach segmentation, AoP estimation, AC and HC measurement, GA prediction) on multi-center external datasets, FetalAgents achieves the best or most competitive results compared with specialized vision models, ultrasound foundation models, and general/medical MLLMs, and supports end-to-end video-stream keyframe ext