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Open AccessArticle

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

1
Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, University of Zaragoza, CIBERER-GCV02 and ISS-Aragon, E-50009 Zaragoza, Spain
2
Department of Paediatrics, Hospital Clínico Universitario “Lozano Blesa”, E-50009 Zaragoza, Spain
3
Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (CSIC-UAM), E-28049 Madrid, Spain
4
Bioscience Research Institute, School of Experimental Sciences, Universidad Francisco de Vitoria, UFV, E-28223 Pozuelo de Alarcón, Spain
5
Section for Functional Genetics, Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany
6
Institute of Science and Technology (IST) Austria, 3400 Klosterneuburg, Austria
7
Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, I-56124 Pisa, Italy
8
Institute for Human Genetics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(3), 1042; https://doi.org/10.3390/ijms21031042
Received: 30 December 2019 / Revised: 1 February 2020 / Accepted: 2 February 2020 / Published: 4 February 2020
(This article belongs to the Special Issue Rare Diseases: Molecular Mechanisms and Therapeutic Strategies (II))
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. View Full-Text
Keywords: Cornelia de Lange syndrome; Face2Gene; Facial recognition; Deep learning Cornelia de Lange syndrome; Face2Gene; Facial recognition; Deep learning
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Latorre-Pellicer, A.; Ascaso, Á.; Trujillano, L.; Gil-Salvador, M.; Arnedo, M.; Lucia-Campos, C.; Antoñanzas-Pérez, R.; Marcos-Alcalde, I.; Parenti, I.; Bueno-Lozano, G.; Musio, A.; Puisac, B.; Kaiser, F.J.; Ramos, F.J.; Gómez-Puertas, P.; Pié, J. Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes. Int. J. Mol. Sci. 2020, 21, 1042.

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