Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Autor: Quentin Hennocq, Marjolaine Willems, Jeanne Amiel, Stéphanie Arpin, Tania Attie-Bitach, Thomas Bongibault, Thomas Bouygues, Valérie Cormier-Daire, Pierre Corre, Klaus Dieterich, Maxime Douillet, Jean Feydy, Eva Galliani, Fabienne Giuliano, Stanislas Lyonnet, Arnaud Picard, Thantrira Porntaveetus, Marlène Rio, Flavien Rouxel, Vorasuk Shotelersuk, Annick Toutain, Kevin Yauy, David Geneviève, Roman H. Khonsari, Nicolas Garcelon
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-52691-3
Popis: Abstract The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p
Databáze: Directory of Open Access Journals
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