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Article Dans Une Revue Prenatal Diagnosis Année : 2024

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

Quentin Hennocq
Thomas Bongibault
  • Fonction : Auteur
Thomas Bouygues
  • Fonction : Auteur
Sandrine Marlin
  • Fonction : Auteur
Jeanne Amiel
  • Fonction : Auteur
Lucile Boutaud
  • Fonction : Auteur
Maxime Douillet
  • Fonction : Auteur
Stanislas Lyonnet
  • Fonction : Auteur
Vèronique Pingault
  • Fonction : Auteur
Arnaud Picard
  • Fonction : Auteur
Marlèe Rio
  • Fonction : Auteur
Tania Attie-Bitach
Roman Khonsari
  • Fonction : Auteur
Nathalie Roux

Résumé

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.
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Dates et versions

hal-04556303 , version 1 (23-04-2024)

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Quentin Hennocq, Nicolas Garcelon, Thomas Bongibault, Thomas Bouygues, Sandrine Marlin, et al.. Artificial intelligence‐based diagnosis in fetal pathology using external ear shapes. Prenatal Diagnosis, 2024, ⟨10.1002/pd.6577⟩. ⟨hal-04556303⟩
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