Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.
Authors
Affiliations (29)
Affiliations (29)
- Shenzhen Luohu People's Hospital (The Third Affiliated Hospital of Shenzhen University), Shenzhen, China.
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
- Christabel Pankhurst Institute, Department of Computer Science, School of Engineering, University of Manchester, Manchester, UK.
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
- Shenzhen University of Advanced Technology General Hospital, Shenzhen, China.
- Shenzhen RayShape Medical Technology Co. Ltd, Shenzhen, China.
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China.
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
- Northwest Women's and Children's Hospital, Xi'an, China.
- Hangzhou Women's Hospital, Hangzhou, China.
- Shanxi Children's Hospital, Taiyuan, China.
- Christabel Pankhurst Institute, Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK.
- Department of Cardiovascular Sciences and Department of Electrical Engineering, Medical Imaging Research Centre (MIRC), Leuven, Belgium.
- Shenzhen University of Advanced Technology General Hospital, Shenzhen, China. [email protected].
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China. [email protected].
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China. [email protected].
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China. [email protected].
- Shenzhen Luohu People's Hospital (The Third Affiliated Hospital of Shenzhen University), Shenzhen, China. [email protected].
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China. [email protected].
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China. [email protected].
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China. [email protected].
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China. [email protected].
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China. [email protected].
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China. [email protected].
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China. [email protected].
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.