AI辅助助产士获取胎儿超声影像，提升偏远地区产前检查可及性。
Development and Evaluation of an AI-Driven Telemedicine System for Prenatal Healthcare
- AI分析盲扫视频，自动识别关键胎儿切面。
- 非专业人员使用低成本设备拍摄，系统识别准确率高。
- 适合资源匮乏地区医疗团队与远程专家协作使用。
在低收入和中等收入国家的农村地区，产科超声检查往往难以获取。本文提出一种人机协同的人工智能系统，协助助产士使用盲扫协议采集具有诊断价值的胎儿图像。系统包含分类模型与基于网络的异步专家评审平台，通过识别盲扫视频中的关键帧，使专家能聚焦于图像解读而非逐帧审查。研究采用少量经过简单培训的助产士使用低成本床旁超声（POCUS）设备采集盲扫视频进行评估。结果显示，该系统能有效从非专业人士采集的扫描中识别出标准胎儿切面。实地测试表明系统易用性强，认知负荷低，具备在资源匮乏地区扩展产前影像检查服务的潜力。
原文摘要 · Abstract (English)
Access to obstetric ultrasound is often limited in low-resource settings, particularly in rural areas of low- and middle-income countries. This work proposes a human-in-the-loop artificial intelligence (AI) system designed to assist midwives in acquiring diagnostically relevant fetal images using blind sweep protocols. The system incorporates a classification model along with a web-based platform for asynchronous specialist reviews. By identifying key frames in blind sweep studies, the AI system allows specialists to concentrate on interpretation rather than having to review entire videos. To evaluate its performance, blind sweep videos captured by a small group of soft-trained midwives using a low-cost Point-of-Care Ultrasound (POCUS) device were analyzed. The system demonstrated promising results in identifying standard fetal planes from sweeps made by non-experts. A field evaluation indicated good usability and a low cognitive workload, suggesting that it has the potential to expand access to prenatal imaging in underserved regions.
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