Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability

Dudding-Byth, Tracy, Baxter, Anne, Holliday, Elizabeth G., Hackett, Anna, O'Donnell, Sheridan, White, Susan M., Attia, John, Brunner, Han, de Vries, Bert, Koolen, David, Kleefstra, Tjitske, Ratwatte, Seshika, Riveros, Carlos, Brain, Steve and Lovell, Brian C. (2017) Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability. BMC Biotechnology, 17 1: 1-9. doi:10.1186/s12896-017-0410-1


Author Dudding-Byth, Tracy
Baxter, Anne
Holliday, Elizabeth G.
Hackett, Anna
O'Donnell, Sheridan
White, Susan M.
Attia, John
Brunner, Han
de Vries, Bert
Koolen, David
Kleefstra, Tjitske
Ratwatte, Seshika
Riveros, Carlos
Brain, Steve
Lovell, Brian C.
Title Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
Journal name BMC Biotechnology   Check publisher's open access policy
ISSN 1472-6750
Publication date 2017-12-19
Year available 2017
Sub-type Article (original research)
DOI 10.1186/s12896-017-0410-1
Open Access Status DOI
Volume 17
Issue 1
Start page 1
End page 9
Total pages 9
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Subject 1305 Biotechnology
Abstract Background: Massively parallel genetic sequencing allows rapid testing of known intellectual disability (ID) genes. However, the discovery of novel syndromic ID genes requires molecular confirmation in at least a second or a cluster of individuals with an overlapping phenotype or similar facial gestalt. Using computer face-matching technology we report an automated approach to matching the faces of non-identical individuals with the same genetic syndrome within a database of 3681 images [1600 images of one of 10 genetic syndrome subgroups together with 2081 control images]. Using the leave-one-out method, two research questions were specified: 1) Using two-dimensional (2D) photographs of individuals with one of 10 genetic syndromes within a database of images, did the technology correctly identify more than expected by chance: i) a top match? ii) at least one match within the top five matches? or iii) at least one in the top 10 with an individual from the same syndrome subgroup? Results: The computer face-matching technology correctly identifies a top match, at least one correct match in the top five and at least one in the top 10 more than expected by chance (P < 0.00001). There was low agreement between the technology and clinicians, with higher accuracy of the technology when results were discordant (P < 0.01) for all syndromes except Kabuki syndrome. Conclusions: Although the accuracy of the computer face-matching technology was tested on images of individuals with known syndromic forms of intellectual disability, the results of this pilot study illustrate the potential utility of face-matching technology within deep phenotyping platforms to facilitate the interpretation of DNA sequencing data for individuals who remain undiagnosed despite testing the known developmental disorder genes.
Keyword 2D photography
Clinical genetics
Computational biology
Computer vision
Dysmorphology
Facial gestalt
Intellectual disability
Phenotyping
Syndromic
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Sun, 31 Dec 2017, 15:53:04 EST