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

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 techn...

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Bibliographic Details
Published in:International journal of molecular sciences 2020-02, Vol.21 (3), p.1042
Main Authors: Latorre-Pellicer, Ana, Ascaso, Ángela, Trujillano, Laura, Gil-Salvador, Marta, Arnedo, Maria, Lucia-Campos, Cristina, Antoñanzas-Pérez, Rebeca, Marcos-Alcalde, Iñigo, Parenti, Ilaria, Bueno-Lozano, Gloria, Musio, Antonio, Puisac, Beatriz, Kaiser, Frank J, Ramos, Feliciano J, Gómez-Puertas, Paulino, Pié, Juan
Format: Article
Language:English
Subjects:
Artificial intelligence
Deep learning
Diagnosis
Genes
Genetic analysis
Genotype & phenotype
Heterogeneity
Image analysis
Image processing
Medical imaging
Neural networks
Patients
Pattern recognition
Phenotypes
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Summary: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.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms21031042