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

Objective: Here 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. Method: We 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. Results: The 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 < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. Conclusion: We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool. Key points What's already known about this topic? center dot Facial analysis of fetuses is crucial for antenatal and post-mortem diagnosis. center dot Prenatal abnormalities of the external ear are common in many syndromes. What does this study add? center dot The aim of this study was to train an AI-based tool on photographs of the external ears of control children and children with two genetic syndromes-CHARGE syndrome and Mandibulo-Facial Dysostosis Guion Almeida type-and to test this tool on photographs of fetal ears, with the aim of supporting the medical genetics diagnosis.