用于检测胎儿口唇腭裂和推进医学教育的人工智能
Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.
摘要(中文译文)
口面裂是最常见的先天性颅面畸形之一，然而由于经验丰富的专家稀缺且该疾病相对罕见，准确的产前检测仍具有挑战性。早期可靠的诊断对于及时进行临床干预和降低相关发病率至关重要。在此，我们展示了一个人工智能系统，该系统基于来自22家医院9，215名胎儿的超过45，139张超声图像进行训练，能够以分别超过93%和95%的敏感度和特异度诊断胎儿口面裂，其表现与资深放射科医生相当，并显著优于初级放射科医生。当作为医疗辅助工具使用时，该系统将初级放射科医生的敏感度提高了6%以上。除直接的诊断辅助外，该系统还能加速临床专业能力的培养。一项涉及24名放射科医生和培训人员的初步研究表明，该模型能够促进罕见疾病专业能力的培养。这种双用途方法为在经验丰富的放射科医生稀缺的环境中提高诊断准确性和专家培训提供了一种可扩展的解决方案。
Abstract
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.
如何引用
AMA
Yuanji Zhang, Yuhao Huang, Haoran Dou, Xiliang Zhu, Chen Ling, Zhong Yang, et al. Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.. Nature communications. 2026; doi:10.1038/s41467-026-74119-4.
APA
Yuanji Zhang, Yuhao Huang, Haoran Dou, Xiliang Zhu, Chen Ling, Zhong Yang, et al (2026). Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.. Nature communications. https://doi.org/10.1038/s41467-026-74119-4
GB/T 7714
Yuanji Zhang, Yuhao Huang, Haoran Dou, Xiliang Zhu, Chen Ling, Zhong Yang, et al. Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.[J]. Nature communications, 2026 doi:10.1038/s41467-026-74119-4.
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