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Diagnostically relevant facial gestalt information from ordinary photos
2014
eLife
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
doi:10.7554/elife.02020
pmid:24963138
pmcid:PMC4067075
fatcat:yamrwvayabhrhff5fhrs7sm62q