Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloEvaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes
Anno di pubblicazione2020
FormatoElettronico
Autore/iLatorre-Pellicer, Ana; Ascaso, Angela; Trujillano, Laura; Gil-Salvador, Marta; Arnedo, Maria; Lucia-Campos, Cristina; Antonanzas-Perez, Rebeca; Marcos-Alcalde, Inigo; Parenti, Ilaria; Bueno-Lozano, Gloria; Musio, Antonio; Puisac, Beatriz; Kaiser, Frank J.; Ramos, Feliciano J.; Gomez-Puertas, Paulino; Pie, Juan
Affiliazioni autoriUniv Zaragoza; Hosp Clin Univ Lozano Blesa; Ctr Biol Mol Severo Ochoa; Univ Francisco Vitoria; Univ Lubeck; IST Austria; CNR; Univ Duisburg Essen
Autori CNR e affiliazioni
  • ANTONIO MUSIO
Lingua/e
  • inglese
AbstractCharacteristic 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.
Lingua abstractinglese
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Pagine totali12
RivistaInternational journal of molecular sciences (Online)
Attiva dal 2000
Editore: MDPI Center, - Basel (Sängergasse 25)
Lingua: inglese
ISSN: 1422-0067
Titolo chiave: International journal of molecular sciences (Online)
Titolo proprio: International journal of molecular sciences. (Online)
Titolo abbreviato: Int. j. mol. sci. (Online)
Numero volume della rivista21
Fascicolo della rivista3
DOI10.3390/ijms21031042
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000522551606028)
Parole chiaveCornelia de Lange syndrome, Face2Gene, Facial recognition, Deep learning
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Data di accettazione-
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Strutture CNR
  • IRGB — Istituto di Ricerca Genetica e Biomedica
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
IJMS (documento privato )
Descrizione: IJMS
Tipo documento: application/pdf