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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2014
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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 |
format | Online Article Text |
id | pubmed-4067075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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