FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914382870478848 |
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| author | Hu, Xiaotian Huang, Junwei Liu, Mingxuan Anmahapong, Kasidit Chen, Yifei Luo, Yitong Huang, Yiming Bai, Xuguang Li, Zihan Liao, Yi Qu, Haibo Tian, Qiyuan |
| author_facet | Hu, Xiaotian Huang, Junwei Liu, Mingxuan Anmahapong, Kasidit Chen, Yifei Luo, Yitong Huang, Yiming Bai, Xuguang Li, Zihan Liao, Yi Qu, Haibo Tian, Qiyuan |
| contents | 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, analyzed by coordinated experts, and synthesized with patient metadata into a structured clinical report. Extensive multi-center external evaluations across eight clinical tasks demonstrate that FetalAgents consistently delivers the most robust and accurate performance when compared against specialized models and multimodal large language models (MLLMs), ultimately providing an auditable, workflow-aligned solution for fetal ultrasound analysis and reporting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09733 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis Hu, Xiaotian Huang, Junwei Liu, Mingxuan Anmahapong, Kasidit Chen, Yifei Luo, Yitong Huang, Yiming Bai, Xuguang Li, Zihan Liao, Yi Qu, Haibo Tian, Qiyuan Computer Vision and Pattern Recognition Multiagent Systems 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, analyzed by coordinated experts, and synthesized with patient metadata into a structured clinical report. Extensive multi-center external evaluations across eight clinical tasks demonstrate that FetalAgents consistently delivers the most robust and accurate performance when compared against specialized models and multimodal large language models (MLLMs), ultimately providing an auditable, workflow-aligned solution for fetal ultrasound analysis and reporting. |
| title | FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis |
| topic | Computer Vision and Pattern Recognition Multiagent Systems |
| url | https://arxiv.org/abs/2603.09733 |