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

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 gest...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735520/
https://www.ncbi.nlm.nih.gov/pubmed/29258477
http://dx.doi.org/10.1186/s12896-017-0410-1
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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.
author_facet 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.
author_sort Dudding-Byth, Tracy
collection PubMed
description 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? 2. Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone? 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12896-017-0410-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-57355202017-12-21 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 Lovell, Brian C. BMC Biotechnol Research Article 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? 2. Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone? 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12896-017-0410-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-19 /pmc/articles/PMC5735520/ /pubmed/29258477 http://dx.doi.org/10.1186/s12896-017-0410-1 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
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.
Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
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
title_full Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
title_fullStr Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
title_full_unstemmed Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
title_short Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
title_sort computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735520/
https://www.ncbi.nlm.nih.gov/pubmed/29258477
http://dx.doi.org/10.1186/s12896-017-0410-1
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