Artificial intelligence for detecting fetal orofacial clefts and advancing medical education
Year: 2026
Type: article
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. Researchers developed an AI system matching senior radiologist accuracy in prenatal orofacial cleft diagnosis, while also serving as an educational tool. In a pilot study, AI-trained groups improved diagnostic performance over traditionally trained peers.
Source: Nature Communications
Authors: Yuanji Zhang, Yuhao Huang, Haoran Dou, Xiliang Zhu, Chen Ling, Zhong Yang, Lianying Liang, Jiuping Li, Siying Liang, Rui Li, Yan Cao, Yuhan Zhang, Jiewei Lai, Yongsong Zhou, Hongyu Zheng, Xinru Gao, Cheng Yu, Liling Shi, Mengqin Yuan, Honglong Li, Xiaoqiong Huang, Chaoyu Chen, Jialin Zhang, Wenxiong Pan, Alejandro F. Frangi, Guangzhi He, Xin Yang, Yi Xiong, Linliang Yin, Xuedong Deng, Dong Ni
Institutions: Shenzhen University, Shenzhen Luohu People's Hospital, University of Manchester, Nanjing Medical University, Shenzhen University of Advanced Technology, The People's Hospital of Guangxi Zhuang Autonomous Region, Northwest Women's and Children's Hospital, Hangzhou Women’s Hospital, Shanxi Provincial Children's Hospital, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, KU Leuven
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: UK Research and Innovation, Yuhan, Cancer Research UK, National Institute for Health and Care Research, British Heart Foundation, Royal Academy of Engineering, National Natural Science Foundation of China, Government of Jiangsu Province, Nanjing Medical University, Shenzhen University, Manchester Biomedical Research Centre, Science and Technology Planning Project of Guangdong Province, Guangxi Key Research and Development Program
OpenAlex ID: W7164912152