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Pantel JT, Hajjir N, Danyel M, Elsner J, Abad-Perez AT, Hansen P, Mundlos S, Spielmann M, Horn D, Ott CE, Mensah MA. Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study. J Med Internet Res 2020; 22:e19263. [PMID: 33090109 PMCID: PMC7644377 DOI: 10.2196/19263] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/26/2020] [Accepted: 07/26/2020] [Indexed: 12/11/2022] Open
Abstract
Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
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Affiliation(s)
- Jean Tori Pantel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Nurulhuda Hajjir
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Klinik für Pädiatrie mit Schwerpunkt Gastroenterologie, Nephrologie und Stoffwechselmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Magdalena Danyel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Center for Rare Diseases, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jonas Elsner
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Angela Teresa Abad-Perez
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Peter Hansen
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Malte Spielmann
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany.,Institute of Human Genetics, University of Lübeck, Lübeck, Germany
| | - Denise Horn
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Claus-Eric Ott
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Martin Atta Mensah
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
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Pantel JT, Zhao M, Mensah MA, Hajjir N, Hsieh TC, Hanani Y, Fleischer N, Kamphans T, Mundlos S, Gurovich Y, Krawitz PM. Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism. J Inherit Metab Dis 2018; 41:533-539. [PMID: 29623569 PMCID: PMC5959962 DOI: 10.1007/s10545-018-0174-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 03/13/2018] [Accepted: 03/18/2018] [Indexed: 11/26/2022]
Abstract
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 systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.
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Affiliation(s)
- Jean T Pantel
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch 2, 10178, Berlin, Germany
| | - Max Zhao
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin A Mensah
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch 2, 10178, Berlin, Germany
| | - Nurulhuda Hajjir
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Tzung-Chien Hsieh
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | | | | | | | - Stefan Mundlos
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | | | - Peter M Krawitz
- Institute of Human Genetics and Medical Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
- GeneTalk, Bonn, Germany.
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Knaus A, Pantel JT, Pendziwiat M, Hajjir N, Zhao M, Hsieh TC, Schubach M, Gurovich Y, Fleischer N, Jäger M, Köhler S, Muhle H, Korff C, Møller RS, Bayat A, Calvas P, Chassaing N, Warren H, Skinner S, Louie R, Evers C, Bohn M, Christen HJ, van den Born M, Obersztyn E, Charzewska A, Endziniene M, Kortüm F, Brown N, Robinson PN, Schelhaas HJ, Weber Y, Helbig I, Mundlos S, Horn D, Krawitz PM. Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis. Genome Med 2018; 10:3. [PMID: 29310717 PMCID: PMC5759841 DOI: 10.1186/s13073-017-0510-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 12/11/2017] [Indexed: 12/17/2022] Open
Abstract
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 subset of GPIBDs, termed multiple congenital anomalies hypotonia seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification. Methods We studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals. Results We found that certain malformations such as Morbus Hirschsprung and diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD. Conclusions Due to the overlapping clinical spectrum of both HPMRS and MCAHS in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0510-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexej Knaus
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.,Berlin-Brandenburg School for Regenerative Therapies, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Jean Tori Pantel
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany
| | - Manuela Pendziwiat
- Department of Neuropediatrics, University Medical Center Schleswig Holstein, 24105, Kiel, Germany
| | - Nurulhuda Hajjir
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany
| | - Max Zhao
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany
| | - Tzung-Chien Hsieh
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Max Schubach
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Berlin Institute of Health (BIH), 10178, Berlin, Germany
| | | | | | - Marten Jäger
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Berlin Institute of Health (BIH), 10178, Berlin, Germany
| | - Sebastian Köhler
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany
| | - Hiltrud Muhle
- Department of Neuropediatrics, University Medical Center Schleswig Holstein, 24105, Kiel, Germany
| | - Christian Korff
- Unité de Neuropédiatrie, Université de Genève, CH-1211, Genève, Switzerland
| | - Rikke S Møller
- Danish Epilepsy Centre, DK-4293, Dianalund, Denmark.,Institute for Regional Health Services Research, University of Southern Denmark, DK-5000, Odense, Denmark
| | - Allan Bayat
- Department of Pediatrics, University Hospital of Hvidovre, 2650, Hvicovre, Denmark
| | - Patrick Calvas
- Service de Génétique Médicale, Hôpital Purpan, CHU, 31059, Toulouse, France
| | - Nicolas Chassaing
- Service de Génétique Médicale, Hôpital Purpan, CHU, 31059, Toulouse, France
| | | | | | | | - Christina Evers
- Genetische Poliklinik, Universitätsklinik Heidelberg, 69120, Heidelberg, Germany
| | - Marc Bohn
- St. Bernward Krankenhaus, 31134, Hildesheim, Germany
| | - Hans-Jürgen Christen
- Kinderkrankenhaus auf der Bult, Hannoversche Kinderheilanstalt, 30173, Hannover, Germany
| | | | - Ewa Obersztyn
- Institute of Mother and Child Department of Molecular Genetics, 01-211, Warsaw, Poland
| | - Agnieszka Charzewska
- Institute of Mother and Child Department of Molecular Genetics, 01-211, Warsaw, Poland
| | - Milda Endziniene
- Neurology Department, Lithuanian University of Health Sciences, 50009, Kaunas, Lithuania
| | - Fanny Kortüm
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Natasha Brown
- Victorian Clinical Genetics Services, Royal Children's Hospital, MCRI, Parkville, Australia.,Department of Clinical Genetics, Austin Health, Heidelberg, Australia
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 06032, Farmington, USA
| | - Helenius J Schelhaas
- Departement of Neurology, Academic Center for Epileptology, 5590, Heeze, The Netherlands
| | - Yvonne Weber
- Department of Neurology and Epileptology and Hertie Institute for Clinical Brain Research, University Tübingen, 72076, Tübingen, Germany
| | - Ingo Helbig
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany.,Pediatric Neurology, Children's Hospital of Philadelphia, 3401, Philadelphia, USA
| | - Stefan Mundlos
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.,Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany
| | - Denise Horn
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany.
| | - Peter M Krawitz
- Institut für Medizinische Genetik und Humangenetik, Charité Universitätsmedizin Berlin, 13353, Berlin, Germany. .,Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany. .,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany.
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