Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

TL;DR

This study presents an AI system trained on over 45,000 ultrasound images that accurately detects fetal orofacial clefts, matching expert radiologists and aiding in training junior radiologists, thus improving prenatal diagnosis and medical education.

Contribution

The paper introduces a scalable AI diagnostic tool for fetal orofacial clefts that also enhances radiologist training, addressing expert scarcity and diagnostic challenges.

Findings

AI system achieves >93% sensitivity and >95% specificity.

System matches senior radiologist performance.

Improves junior radiologists' sensitivity by over 6%.

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.