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dc.contributor.authorLatorre Peciller, Ana
dc.contributor.authorAscaso, Ángela
dc.contributor.authorTrujillano, Laura
dc.contributor.authorGil Salvador, Marta
dc.contributor.authorArnedo, María
dc.contributor.authorLucía Campos, Cristina
dc.contributor.authorAntoñanzas Pérez, Rebeca
dc.contributor.authorMarcos Alcalde, Íñigo 
dc.contributor.authorParenti, Ilaria
dc.contributor.authorBueno Lozano, Gloria
dc.contributor.authorMusio, Antonio
dc.contributor.authorPuisac, Beatriz
dc.contributor.authorKaiser, Frank J.
dc.contributor.authorRamos, Feliciano J.
dc.contributor.authorGómez Puertas, Paulino
dc.contributor.authorPie, Juan
dc.date.accessioned2020-10-19T09:33:01Z
dc.date.available2020-10-19T09:33:01Z
dc.date.issued2020
dc.identifier.issn1422-0067spa
dc.identifier.urihttp://hdl.handle.net/10641/2007
dc.description.abstractCharacteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly di cult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of a ected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to a ect the prediction accuracy, whereas our results indicate a correlation between the clinical score and a ected genes. Furthermore, each gene presents a di erent pattern recognition that may be used to develop new neural networks with the goal of separating di erent genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.spa
dc.language.isoengspa
dc.publisherInternational Journal of Molecular Sciencesspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectCornelia de Lange syndromespa
dc.subjectFace2Genespa
dc.subjectFacial recognitionspa
dc.subjectDeep learningspa
dc.titleEvaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes.spa
dc.typearticlespa
dc.description.versionpost-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent4172 KBspa
dc.identifier.doi10.3390/ijms21031042spa
dc.relation.publisherversionhttps://www.mdpi.com/1422-0067/21/3/1042spa


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