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Yaron Gurovich
Deep learningGeneticsBioinformaticsMedical geneticsBiology
13Publications
4H-index
90Citations
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Publications 9
Newest
#1Tzung-Chien Hsieh (Humboldt University of Berlin)H-Index: 2
#2Martin A. Mensah (Humboldt University of Berlin)H-Index: 3
Last. Peter M. Krawitz (University of Bonn)H-Index: 2
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Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different mono...
3 CitationsSource
#1Yaron GurovichH-Index: 4
#2Yair HananiH-Index: 4
Last. Karen W. Gripp (DuPont)H-Index: 18
view all 12 authors...
Syndromic genetic conditions, in aggregate, affect 8% of the population1. Many syndromes have recognizable facial features2 that are highly informative to clinical geneticists3–5. Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification6–9. However, these technologies identified only a few disease phenotypes, limiting their role in clinical settings, where hundreds of diagnoses must be considered. Here we present a faci...
32 CitationsSource
#1Dekel GelbmanH-Index: 2
#2Yaron GurovichH-Index: 4
#1Jean Tori Pantel (Humboldt University of Berlin)H-Index: 3
#2Max Zhao (Humboldt University of Berlin)H-Index: 3
Last. Peter M. Krawitz (Humboldt University of Berlin)H-Index: 1
view all 11 authors...
Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systemat...
10 CitationsSource
Facial analysis technologies have recently measured up to the capabilities of expert clinicians in syndrome identification. To date, these technologies could only identify phenotypes of a few diseases, limiting their role in clinical settings where hundreds of diagnoses must be considered. We developed a facial analysis framework, DeepGestalt, using computer vision and deep learning algorithms, that quantifies similarities to hundreds of genetic syndromes based on unconstrained 2D images. DeepGe...
9 Citations
#1Alexej KnausH-Index: 5
#2Jean Tori Pantel (Charité)H-Index: 3
Last. Peter Krawitz (Charité)H-Index: 27
view all 36 authors...
Glycosylphosphatidylinositol biosynthesis defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of hyperphosphatasia with mental retardation syndrome (HPMRS) and to distinguish them from another subset of G...
26 CitationsSource
#1Jean Tori Pantel (Charité)H-Index: 3
#2Max Zhao (Charité)H-Index: 3
Last. Peter KrawitzH-Index: 27
view all 11 authors...
Significant improvements in automated image analysis have been achieved over the recent years and tools are now increasingly being used in computer-assisted syndromology. However, the recognizability of the facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic a...
4 CitationsSource
#1Alexej Knaus (Humboldt University of Berlin)H-Index: 5
#2Jean Tori Pantel (Charité)H-Index: 3
Last. Peter M. Krawitz (University Hospital Bonn)H-Index: 2
view all 36 authors...
Background: Glycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another...
Source
Sep 1, 2016 in ICIP (International Conference on Image Processing)
#1Yaron GurovichH-Index: 4
Last. Yair HananiH-Index: 4
view all 3 authors...
Deep methods based on Convolutional Neural Networks serve as accurate facial points and body parts detectors. However, most methods do not provide a confidence score for the quality of the localization process. In real world applications, such a score could be invaluable. We, therefore, study the problem of estimating the success of the localization process during test time. Our method is based on mapping the network activation features to the area under the point-accuracy-curve. Our method grea...
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