Literature DB >> 28691769

Automatic recognition of the XLHED phenotype from facial images.

Smail Hadj-Rabia1, Holm Schneider2, Elena Navarro3, Ophir Klein4, Neil Kirby5, Kenneth Huttner5,6, Lior Wolf7,8, Melanie Orin7, Sigrun Wohlfart2, Christine Bodemer1, Dorothy K Grange9.   

Abstract

X-linked hypohidrotic ectodermal dysplasia (XLHED) is a genetic disorder that affects ectodermal structures and presents with a characteristic facial appearance. The ability of automated facial recognition technology to detect the phenotype from images was assessed . In Phase 1 of this study we examined if the age of male patients affected the technology's recognition. In Phase 2 we investigated how well the technology discriminated affected males cases from female carriers and from individuals with other ectodermal dysplasia syndromes. The system detected XLHED to be the most likely diagnosis in all genetically confirmed affected male patients of all ages, and in 55% of heterozygous females. Interestingly, patients with other ED syndromes were also detected by the XLHED-targeted analysis, consistent with shared developmental features. Thus the automated facial recognition system represents a promising non-invasive technology to screen patients at all ages for a possible diagnosis of ectodermal dysplasia, with greatest sensitivity and specificity for males affected with XLHED.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  anhidrotic/hypohidrotic ectodermal dysplasia; automated facial recognition; dermatology; dysmorphology; pediatrics

Mesh:

Year:  2017        PMID: 28691769     DOI: 10.1002/ajmg.a.38343

Source DB:  PubMed          Journal:  Am J Med Genet A        ISSN: 1552-4825            Impact factor:   2.802


  11 in total

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Journal:  Eur J Hum Genet       Date:  2019-07-18       Impact factor: 4.246

2.  The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers.

Authors:  Giulia Pascolini; Nicole Fleischer; Alessandro Ferraris; Silvia Majore; Paola Grammatico
Journal:  J Hum Genet       Date:  2019-05-13       Impact factor: 3.172

3.  Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome.

Authors:  Zhouxian Pan; Zhen Shen; Huijuan Zhu; Yin Bao; Siyu Liang; Shirui Wang; Xiangying Li; Lulu Niu; Xisong Dong; Xiuqin Shang; Shi Chen; Hui Pan; Gang Xiong
Journal:  Endocrine       Date:  2020-11-10       Impact factor: 3.633

4.  Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt.

Authors:  Flavien Rouxel; Kevin Yauy; Guilaine Boursier; Vincent Gatinois; Mouna Barat-Houari; Elodie Sanchez; Didier Lacombe; Stéphanie Arpin; Fabienne Giuliano; Damien Haye; Marlène Rio; Annick Toutain; Klaus Dieterich; Elise Brischoux-Boucher; Sophie Julia; Mathilde Nizon; Alexandra Afenjar; Boris Keren; Aurelia Jacquette; Sebastien Moutton; Marie-Line Jacquemont; Claire Duflos; Yline Capri; Jeanne Amiel; Patricia Blanchet; Stanislas Lyonnet; Damien Sanlaville; David Genevieve
Journal:  Eur J Hum Genet       Date:  2021-11-22       Impact factor: 5.351

5.  eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.

Authors:  Mattia Bosio; Oliver Drechsel; Rubayte Rahman; Francesc Muyas; Raquel Rabionet; Daniela Bezdan; Laura Domenech Salgado; Hyun Hor; Jean-Jacques Schott; Francina Munell; Roger Colobran; Alfons Macaya; Xavier Estivill; Stephan Ossowski
Journal:  Hum Mutat       Date:  2019-05-21       Impact factor: 4.878

6.  Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.

Authors:  Christoffer Nellåker; Fowzan S Alkuraya; Gareth Baynam; Raphael A Bernier; Francois P J Bernier; Vanessa Boulanger; Michael Brudno; Han G Brunner; Jill Clayton-Smith; Benjamin Cogné; Hugh J S Dawkins; Bert B A deVries; Sofia Douzgou; Tracy Dudding-Byth; Evan E Eichler; Michael Ferlaino; Karen Fieggen; Helen V Firth; David R FitzPatrick; Dylan Gration; Tudor Groza; Melissa Haendel; Nina Hallowell; Ada Hamosh; Jayne Hehir-Kwa; Marc-Phillip Hitz; Mark Hughes; Usha Kini; Tjitske Kleefstra; R Frank Kooy; Peter Krawitz; Sébastien Küry; Melissa Lees; Gholson J Lyon; Stanislas Lyonnet; Julien L Marcadier; Stephen Meyn; Veronika Moslerová; Juan M Politei; Cathryn C Poulton; F Lucy Raymond; Margot R F Reijnders; Peter N Robinson; Corrado Romano; Catherine M Rose; David C G Sainsbury; Lyn Schofield; Vernon R Sutton; Marek Turnovec; Anke Van Dijck; Hilde Van Esch; Andrew O M Wilkie
Journal:  Front Genet       Date:  2019-07-29       Impact factor: 4.599

7.  A deep learning-based diagnostic tool for identifying various diseases via facial images.

Authors:  Omneya Attallah
Journal:  Digit Health       Date:  2022-09-10

8.  Diagnostic performance of artificial intelligence to detect genetic diseases with facial phenotypes: A protocol for systematic review and meta analysis.

Authors:  Bosheng Qin; Qiyao Quan; Jingchao Wu; Letian Liang; Dongxiao Li
Journal:  Medicine (Baltimore)       Date:  2020-07-02       Impact factor: 1.817

9.  Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.

Authors:  Jean Tori Pantel; Nurulhuda Hajjir; Magdalena Danyel; Jonas Elsner; Angela Teresa Abad-Perez; Peter Hansen; Stefan Mundlos; Malte Spielmann; Denise Horn; Claus-Eric Ott; Martin Atta Mensah
Journal:  J Med Internet Res       Date:  2020-10-22       Impact factor: 5.428

Review 10.  Clinical Exome Reanalysis: Current Practice and Beyond.

Authors:  Jianling Ji; Marco L Leung; Samuel Baker; Joshua L Deignan; Avni Santani
Journal:  Mol Diagn Ther       Date:  2021-07-20       Impact factor: 4.476

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