88167 20230706131403.0 doi 10.3390/ijms21031042 sideral 116235 ART-2020-116235 eng Latorre-Pellicer, Ana (orcid)0000-0002-4703-6620 Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes 2020 Access copy available to the general public Unrestricted Characteristic 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 difficult 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 affected 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 affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS info:eu-repo/grantAgreement/ES/DGA/B32-17R info:eu-repo/grantAgreement/ES/FIS/PI19-01860 info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094434-B-100 info:eu-repo/grantAgreement/ES/MINECO/RTC-2017-6494-1 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 5.923 2020 BIOCHEMISTRY & MOLECULAR BIOLOGY 67 / 296 = 0.226 2020 Q1 T1 CHEMISTRY, MULTIDISCIPLINARY 49 / 178 = 0.275 2020 Q2 T1 1.455 2020 Catalysis 2020 Q1 Computer Science Applications 2020 Q1 Inorganic Chemistry 2020 Q1 Spectroscopy 2020 Q1 Molecular Biology 2020 Q1 Organic Chemistry 2020 Q1 Physical and Theoretical Chemistry 2020 Q1 Medicine (miscellaneous) 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ascaso, Ángela Trujillano, Laura Gil-Salvador, Marta Arnedo, María Universidad de Zaragoza (orcid)0000-0001-9962-2157 Lucia-Campos, Cristina Universidad de Zaragoza Antoñanzas-Pérez, Rebeca Universidad de Zaragoza Marcos-Alcalde, Iñigo Parenti, Ilaria Bueno-Lozano, Gloria Universidad de Zaragoza (orcid)0000-0002-0902-387X Musio, Antonio Puisac, Beatriz Universidad de Zaragoza (orcid)0000-0003-0170-7326 Kaiser, Frank J. Ramos, Feliciano J. Universidad de Zaragoza (orcid)0000-0002-5732-2209 Gómez-Puertas, Paulino Pié, Juan Universidad de Zaragoza (orcid)0000-0003-3203-6254 1012 410 Universidad de Zaragoza Dpto. Farmac.Fisiol.y Med.L.F. Área Fisiología 1012 X Universidad de Zaragoza Dpto. Farmac.Fisiol.y Med.L.F. Proy. investigación DUA 1011 670 Universidad de Zaragoza Dpto. Microb.Ped.Radio.Sal.Pú. Área Pediatría 21, 3 (2020), 1042 [12 pp.] Int. j. mol. sci. International Journal of Molecular Sciences 1661-6596 3958886 http://zaguan.unizar.es/record/88167/files/texto_completo.pdf Versión publicada 469414 http://zaguan.unizar.es/record/88167/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:88167 articulos driver 2023-07-06-12:20:35 ARTICLE