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Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt.

dc.rights.licenseopenen_US
dc.contributor.authorROUXEL, Flavien
dc.contributor.authorYAUY, Kevin
dc.contributor.authorBOURSIER, Guilaine
dc.contributor.authorGATINOIS, Vincent
dc.contributor.authorBARAT-HOUARI, Mouna
dc.contributor.authorSANCHEZ, Elodie
hal.structure.identifierLaboratoire Maladies Rares: Génétique et Métabolisme (Bordeaux) [U1211 INSERM/MRGM]
dc.contributor.authorLACOMBE, Didier
dc.contributor.authorARPIN, Stephanie
dc.contributor.authorGIULIANO, Fabienne
dc.contributor.authorHAYE, Damien
dc.contributor.authorTOUTAIN, Annick
dc.contributor.authorDIETERICH, Klaus
dc.contributor.authorBRISCHOUX-BOUCHER, Elise
dc.contributor.authorJULIA, Sophie
dc.contributor.authorNIZON, Mathilde
dc.contributor.authorAFENJAR, Alexandra
dc.contributor.authorKEREN, Boris
dc.contributor.authorJACQUETTE, Aurelia
dc.contributor.authorMOUTTON, Sebastien
dc.contributor.authorJACQUEMONT, Marie-Line
dc.contributor.authorDUFLOS, Claire
dc.contributor.authorCAPRI, Yline
dc.contributor.authorAMIEL, Jeanne
dc.contributor.authorBLANCHET, Patricia
dc.contributor.authorLYONNET, Stanislas
dc.contributor.authorSANLAVILLE, Damien
dc.contributor.authorGENEVIEVE, David
dc.date.accessioned2023-06-23T13:25:55Z
dc.date.available2023-06-23T13:25:55Z
dc.date.issued2022-06-01
dc.identifier.issn1476-5438en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/182794
dc.description.abstractEnKabuki syndrome (KS) is a rare genetic disorder caused by mutations in two major genes, KMT2D and KDM6A, that are responsible for Kabuki syndrome 1 (KS1, OMIM147920) and Kabuki syndrome 2 (KS2, OMIM300867), respectively. We lack a description of clinical signs to distinguish KS1 and KS2. We used facial morphology analysis to detect any facial morphological differences between the two KS types. We used a facial-recognition algorithm to explore any facial morphologic differences between the two types of KS. We compared several image series of KS1 and KS2 individuals, then compared images of those of Caucasian origin only (12 individuals for each gene) because this was the main ethnicity in this series. We also collected 32 images from the literature to amass a large series. We externally validated results obtained by the algorithm with evaluations by trained clinical geneticists using the same set of pictures. Use of the algorithm revealed a statistically significant difference between each group for our series of images, demonstrating a different facial morphotype between KS1 and KS2 individuals (mean area under the receiver operating characteristic curve = 0.85 [p = 0.027] between KS1 and KS2). The algorithm was better at discriminating between the two types of KS with images from our series than those from the literature (p = 0.0007). Clinical geneticists trained to distinguished KS1 and KS2 significantly recognised a unique facial morphotype, which validated algorithm findings (p = 1.6e-11). Our deep-neural-network-driven facial-recognition algorithm can reveal specific composite gestalt images for KS1 and KS2 individuals.
dc.language.isoENen_US
dc.title.enUsing deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt.
dc.title.alternativeEur J Hum Geneten_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41431-021-00994-8en_US
dc.identifier.pubmed34803161en_US
bordeaux.journalEuropean Journal of Human Geneticsen_US
bordeaux.page682-686en_US
bordeaux.volume30en_US
bordeaux.hal.laboratoriesMaladies Rares : Génétique et Métabolisme (MRGM) - UMR 1211en_US
bordeaux.issue6en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcepubmed
hal.exportfalse
workflow.import.sourcepubmed
dc.rights.ccPas de Licence CCen_US
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