Artificial intelligence‐based diagnosis in fetal pathology using external ear shapes

ObjectiveHere we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses—=CHARGE and Mandibulo‐Facial Dysostosis Guion Almeida type (MFDGA)—versus controls.MethodWe trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes.ResultsThe training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%–84.1%, p