Automatic recognition of the XLHED phenotype from facial images. Issue 9 (10th July 2017)

• Record Type: Journal Article
• Authors: Hadj‐Rabia, Smail; Schneider, Holm; Navarro, Elena; Klein, Ophir; Kirby, Neil; Huttner, Kenneth; Wolf, Lior; Orin, Melanie; Wohlfart, Sigrun; Bodemer, Christine; Grange, Dorothy K.
• Is Part Of: American journal of medical genetics. Volume 173:Issue 9(2017)
• Page Start: 2408
• Page End: 2414
• Publication Date: 2017-07-10
• Subjects: anhidrotic/hypohidrotic ectodermal dysplasia -- automated facial recognition -- dermatology -- dysmorphology -- pediatrics; Medical genetics -- Periodicals; 616.14205
• DOI: 10.1002/ajmg.a.38343
• Languages: English
• ISSNs: 1552-4825

Abstract

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.