AI可精准识别胎儿面部裂口，助医生早诊断并加速培训。
Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
- 用4.5万张超声图训练AI，自动识别胎儿面部裂口。
- 检测灵敏度超93%，特异性超95%，媲美资深放射科医生。
- 提升新手医生诊断能力，适合资源匮乏地区使用。
面部裂口是常见的先天性颅面畸形，但因经验专家稀缺且病情罕见，产前准确诊断仍具挑战。早期可靠诊断对及时临床干预、降低并发症至关重要。本文展示，基于22家医院9,215名胎儿的45,139张超声图像训练的AI系统，在检测胎儿面部裂口方面，灵敏度超过93%，特异性超过95%，表现与资深放射科医生相当，并显著优于初级医生。作为医疗辅助工具，该系统使初级医生的检测灵敏度提升超6%。此外，初步研究显示，该模型能加速放射科医生和学员对罕见病的认知发展。这一双重用途方案为经验不足地区的诊断准确性和专业人才培养提供了可扩展的解决方案。
原文摘要 · Abstract (English)
Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.
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