Diagnostically relevant facial gestalt information from ordinary photos

Author:

Ferry Quentin12,Steinberg Julia23,Webber Caleb2,FitzPatrick David R4,Ponting Chris P2,Zisserman Andrew1,Nellåker Christoffer2

Affiliation:

1. Department of Engineering Science, University of Oxford, Oxford, United Kingdom

2. Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

3. The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

4. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, United Kingdom

Abstract

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.

Funder

Medical Research Council

Wellcome Trust

European Research Council

Oxford Biomedical Research Centre

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference64 articles.

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