1. Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.
- Author
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Hennocq Q, Garcelon N, Bongibault T, Bouygues T, Marlin S, Amiel J, Boutaud L, Douillet M, Lyonnet S, Pingault V, Picard A, Rio M, Attie-Bitach T, Khonsari RH, and Roux N
- Subjects
- Humans, Female, Pregnancy, CHARGE Syndrome diagnosis, Mandibulofacial Dysostosis diagnosis, Mandibulofacial Dysostosis diagnostic imaging, Mandibulofacial Dysostosis pathology, Case-Control Studies, Prenatal Diagnosis methods, Male, Ear, External pathology, Artificial Intelligence
- Abstract
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., (© 2024 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.)
- Published
- 2024
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