Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
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arXiv
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| Natura: | Preprint |
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2026
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| author | Zhang, Yuanji Huang, Yuhao Dou, Haoran Zhu, Xiliang Ling, Chen Yang, Zhong Liang, Lianying Li, Jiuping Liang, Siying Li, Rui Cao, Yan Zhang, Yuhan Lai, Jiewei Zhou, Yongsong Zheng, Hongyu Gao, Xinru Yu, Cheng Shi, Liling Yuan, Mengqin Li, Honglong Huang, Xiaoqiong Chen, Chaoyu Zhang, Jialin Pan, Wenxiong Frangi, Alejandro F. He, Guangzhi Yang, Xin Xiong, Yi Yin, Linliang Deng, Xuedong Ni, Dong |
| author_facet | Zhang, Yuanji Huang, Yuhao Dou, Haoran Zhu, Xiliang Ling, Chen Yang, Zhong Liang, Lianying Li, Jiuping Liang, Siying Li, Rui Cao, Yan Zhang, Yuhan Lai, Jiewei Zhou, Yongsong Zheng, Hongyu Gao, Xinru Yu, Cheng Shi, Liling Yuan, Mengqin Li, Honglong Huang, Xiaoqiong Chen, Chaoyu Zhang, Jialin Pan, Wenxiong Frangi, Alejandro F. He, Guangzhi Yang, Xin Xiong, Yi Yin, Linliang Deng, Xuedong Ni, Dong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06522 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education Zhang, Yuanji Huang, Yuhao Dou, Haoran Zhu, Xiliang Ling, Chen Yang, Zhong Liang, Lianying Li, Jiuping Liang, Siying Li, Rui Cao, Yan Zhang, Yuhan Lai, Jiewei Zhou, Yongsong Zheng, Hongyu Gao, Xinru Yu, Cheng Shi, Liling Yuan, Mengqin Li, Honglong Huang, Xiaoqiong Chen, Chaoyu Zhang, Jialin Pan, Wenxiong Frangi, Alejandro F. He, Guangzhi Yang, Xin Xiong, Yi Yin, Linliang Deng, Xuedong Ni, Dong Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.06522 |