Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes.
Cornelia de Lange syndrome
Deep learning
Face2Gene
Facial recognition
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
04 Feb 2020
04 Feb 2020
Historique:
received:
30
12
2019
revised:
01
02
2020
accepted:
02
02
2020
entrez:
9
2
2020
pubmed:
9
2
2020
medline:
18
11
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32033219
pii: ijms21031042
doi: 10.3390/ijms21031042
pmc: PMC7038094
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Spanish Ministry of Health - Fondo de Investigación Sanitaria (FIS)
ID : PI19/01860
Organisme : Spanish Ministry of Science, Innovation and Universities/State Research Agency
ID : RTC-2017-6494-1
Organisme : Spanish Ministry of Science, Innovation and Universities/State Research Agency
ID : RTI2018-094434-B-I00
Organisme : Diputación General de Aragón - FEDER: European Social Fund
ID : Grupo de Referencia B32_17R
Organisme : European JPIAMR-VRI
ID : network "CONNECT"
Organisme : Fondazione Pisa
ID : Fondazione Pisa
Organisme : Medical Faculty of the University of Lübeck
ID : J09-2017
Organisme : German Federal Ministry of Education and Research
ID : CHROMATIN-Net 01GM1520C
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
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