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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doaj-aee2ed28d47f4125934729daac964ed12021-05-04T23:12:40ZengeLife Sciences Publications LtdeLife2050-084X2014-06-01310.7554/eLife.02020Diagnostically relevant facial gestalt information from ordinary photosQuentin Ferry0Julia Steinberg1Caleb Webber2David R FitzPatrick3Chris P Ponting4Andrew Zisserman5Christoffer Nellåker6Department of Engineering Science, University of Oxford, Oxford, United Kingdom; Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United KingdomMedical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United KingdomMedical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United KingdomMedical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, United KingdomMedical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United KingdomDepartment of Engineering Science, University of Oxford, Oxford, United KingdomMedical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United KingdomCraniofacial 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.https://elifesciences.org/articles/02020phenotypingcomputer visionclinical geneticscomputational biology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Quentin Ferry Julia Steinberg Caleb Webber David R FitzPatrick Chris P Ponting Andrew Zisserman Christoffer Nellåker |
spellingShingle |
Quentin Ferry Julia Steinberg Caleb Webber David R FitzPatrick Chris P Ponting Andrew Zisserman Christoffer Nellåker Diagnostically relevant facial gestalt information from ordinary photos eLife phenotyping computer vision clinical genetics computational biology |
author_facet |
Quentin Ferry Julia Steinberg Caleb Webber David R FitzPatrick Chris P Ponting Andrew Zisserman Christoffer Nellåker |
author_sort |
Quentin Ferry |
title |
Diagnostically relevant facial gestalt information from ordinary photos |
title_short |
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_sort |
diagnostically relevant facial gestalt information from ordinary photos |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2014-06-01 |
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. |
topic |
phenotyping computer vision clinical genetics computational biology |
url |
https://elifesciences.org/articles/02020 |
work_keys_str_mv |
AT quentinferry diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT juliasteinberg diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT calebwebber diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT davidrfitzpatrick diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT chrispponting diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT andrewzisserman diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos AT christoffernellaker diagnosticallyrelevantfacialgestaltinformationfromordinaryphotos |
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