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Diagnostically relevant facial gestalt information from ordinary photos

Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This al...

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Autores principales: Ferry, Quentin, Steinberg, Julia, Webber, Caleb, FitzPatrick, David R, Ponting, Chris P, Zisserman, Andrew, Nellåker, Christoffer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067075/
https://www.ncbi.nlm.nih.gov/pubmed/24963138
http://dx.doi.org/10.7554/eLife.02020
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author Ferry, Quentin
Steinberg, Julia
Webber, Caleb
FitzPatrick, David R
Ponting, Chris P
Zisserman, Andrew
Nellåker, Christoffer
author_facet Ferry, Quentin
Steinberg, Julia
Webber, Caleb
FitzPatrick, David R
Ponting, Chris P
Zisserman, Andrew
Nellåker, Christoffer
author_sort Ferry, Quentin
collection PubMed
description Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons. DOI:http://dx.doi.org/10.7554/eLife.02020.001
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spelling pubmed-40670752014-06-27 Diagnostically relevant facial gestalt information from ordinary photos Ferry, Quentin Steinberg, Julia Webber, Caleb FitzPatrick, David R Ponting, Chris P Zisserman, Andrew Nellåker, Christoffer eLife Human Biology and Medicine Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons. DOI:http://dx.doi.org/10.7554/eLife.02020.001 eLife Sciences Publications, Ltd 2014-06-24 /pmc/articles/PMC4067075/ /pubmed/24963138 http://dx.doi.org/10.7554/eLife.02020 Text en © 2014, Ferry et al https://creativecommons.org/licenses/by/3.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Human Biology and Medicine
Ferry, Quentin
Steinberg, Julia
Webber, Caleb
FitzPatrick, David R
Ponting, Chris P
Zisserman, Andrew
Nellåker, Christoffer
Diagnostically relevant facial gestalt information from ordinary photos
title Diagnostically relevant facial gestalt information from ordinary photos
title_full Diagnostically relevant facial gestalt information from ordinary photos
title_fullStr Diagnostically relevant facial gestalt information from ordinary photos
title_full_unstemmed Diagnostically relevant facial gestalt information from ordinary photos
title_short Diagnostically relevant facial gestalt information from ordinary photos
title_sort diagnostically relevant facial gestalt information from ordinary photos
topic Human Biology and Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067075/
https://www.ncbi.nlm.nih.gov/pubmed/24963138
http://dx.doi.org/10.7554/eLife.02020
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