Affiliation:
1. Imagine Institute INSERM UMR1163 Paris France
2. Service de Chirurgie Maxillo‐Faciale et Chirurgie Plastique Hôpital Necker – Enfants Malades Assistance Publique – Hôpitaux de Paris Paris France
3. Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE Filière Maladies Rares TeteCou Paris France
4. Faculté de Médecine Université de Paris Cité Paris France
5. Laboratoire ‘Forme et Croissance Du Crâne’ Hôpital Necker‐Enfants Malades Assistance Publique‐Hôpitaux de Paris Paris France
6. Service de Médecine Génomique des Maladies Rares Hôpital Necker – Enfants Malades Assistance Publique – Hôpitaux de Paris Paris France
Abstract
AbstractObjectiveHere 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 < 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.ConclusionWe report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.
Funder
Agence Nationale de la Recherche
National University of Singapore