Automatic recognition of the XLHED phenotype from facial images

Smail Hadj-Rabia, Holm Schneider, Elena Navarro, Ophir Klein, Neil Kirby, Kenneth Huttner, Lior Wolf, Melanie Orin, Sigrun Wohlfart, Christine Bodemer, Dorothy K. Grange

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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.

Original languageEnglish
Pages (from-to)2408-2414
Number of pages7
JournalAmerican Journal of Medical Genetics, Part A
Volume173
Issue number9
DOIs
StatePublished - Sep 2017

Keywords

  • anhidrotic/hypohidrotic ectodermal dysplasia
  • automated facial recognition
  • dermatology
  • dysmorphology
  • pediatrics

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