Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes.
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2020
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International Journal of Molecular Sciences
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Abstract
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 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.
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Keywords
Cornelia de Lange syndrome, Face2Gene, Facial recognition, Deep learning