Latorre Peciller, AnaAscaso, ÁngelaTrujillano, LauraGil Salvador, MartaArnedo, MaríaLucía Campos, CristinaAntoñanzas Pérez, RebecaMarcos Alcalde, ÍñigoParenti, IlariaBueno Lozano, GloriaMusio, AntonioPuisac, BeatrizKaiser, Frank J.Ramos, Feliciano J.Gómez Puertas, PaulinoPie, Juan2020-10-192020-10-1920201422-0067http://hdl.handle.net/10641/2007Characteristic 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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Cornelia de Lange syndromeFace2GeneFacial recognitionDeep learningjournal articleopen access10.3390/ijms21031042