Frontiers in Pediatrics (Aug 2023)

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

  • Quentin Hennocq,
  • Quentin Hennocq,
  • Quentin Hennocq,
  • Thomas Bongibault,
  • Thomas Bongibault,
  • Sandrine Marlin,
  • Sandrine Marlin,
  • Jeanne Amiel,
  • Jeanne Amiel,
  • Tania Attie-Bitach,
  • Tania Attie-Bitach,
  • Geneviève Baujat,
  • Geneviève Baujat,
  • Lucile Boutaud,
  • Georges Carpentier,
  • Pierre Corre,
  • Pierre Corre,
  • Françoise Denoyelle,
  • François Djate Delbrah,
  • Maxime Douillet,
  • Eva Galliani,
  • Wuttichart Kamolvisit,
  • Wuttichart Kamolvisit,
  • Stanislas Lyonnet,
  • Stanislas Lyonnet,
  • Dan Milea,
  • Véronique Pingault,
  • Véronique Pingault,
  • Thantrira Porntaveetus,
  • Thantrira Porntaveetus,
  • Sandrine Touzet-Roumazeille,
  • Marjolaine Willems,
  • Arnaud Picard,
  • Marlène Rio,
  • Marlène Rio,
  • Nicolas Garcelon,
  • Roman H. Khonsari,
  • Roman H. Khonsari,
  • Roman H. Khonsari

DOI
https://doi.org/10.3389/fped.2023.1171277
Journal volume & issue
Vol. 11

Abstract

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IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.

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