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.

翻译：口面裂是最常见的先天性颅面异常之一，然而由于经验丰富的专科医生稀缺且该病症相对罕见，准确的产前检测仍具挑战。早期可靠的诊断对于实现及时临床干预和降低相关发病率至关重要。本文展示了一种人工智能系统，该系统基于来自22家医院9,215个胎儿的45,139张超声图像训练，对口面裂的诊断敏感性和特异性分别超过93%和95%，其性能与资深放射科医生相当，并显著优于初级放射科医生。当作为医疗协作者使用时，该系统可将初级放射科医生的敏感性提升超过6%。除直接诊断辅助外，该系统还能加速临床专业技能的培养。一项涉及24名放射科医生及学员的试点研究表明，该模型可提升针对罕见病症的专业能力发展。这种双重用途的方法为在资深放射科医生稀缺的环境中提高诊断准确性和专家培训水平，提供了一种可扩展的解决方案。