Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
2026-03-06 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors developed an AI system that looks at ultrasound images to detect orofacial clefts in unborn babies, which are hard to spot early on. They trained the system with thousands of images and found it was as good as expert doctors and better than less experienced ones. Using the AI helped junior doctors improve their detection skills too. The system not only helps with diagnosis but also supports training for spotting rare conditions where experts are limited.
orofacial cleftscongenital abnormalitiesultrasound imagingartificial intelligencemedical diagnosissensitivityspecificityradiologist trainingclinical intervention
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
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.