{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:11:58Z","timestamp":1787850718206,"version":"build-2784847793"},"reference-count":16,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T00:00:00Z","timestamp":1522886400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T00:00:00Z","timestamp":1522886400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["KR 3985\/7\u20103"],"award-info":[{"award-number":["KR 3985\/7\u20103"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J of Inher Metab Disea"],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer\u2010assisted 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\u2010learned 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\u2010Lemli\u2010Opitz syndrome as another inborn error of metabolism and Nicolaides\u2010Baraitser 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\u2009=\u2009[10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype\u2010specific 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\u2010assisted syndrome classification can still be improved by enlarging the available training sets.<\/jats:p>","DOI":"10.1007\/s10545-018-0174-3","type":"journal-article","created":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T10:07:59Z","timestamp":1522922879000},"page":"533-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Advances in computer\u2010assisted syndrome recognition by the example of inborn errors of metabolism"],"prefix":"10.1002","volume":"41","author":[{"given":"Jean T.","family":"Pantel","sequence":"first","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"},{"name":"Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn Rheinische Friedrich\u2010Wilhelms\u2010Universit\u00e4t Bonn Bonn Germany"},{"name":"Berlin Institute of Health (BIH) Anna\u2010Louisa\u2010Karsch 2 Berlin 10178 Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"},{"name":"Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn Rheinische Friedrich\u2010Wilhelms\u2010Universit\u00e4t Bonn Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin A.","family":"Mensah","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"},{"name":"Berlin Institute of Health (BIH) Anna\u2010Louisa\u2010Karsch 2 Berlin 10178 Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurulhuda","family":"Hajjir","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzung\u2010Chien","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"},{"name":"Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn Rheinische Friedrich\u2010Wilhelms\u2010Universit\u00e4t Bonn Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yair","family":"Hanani","sequence":"additional","affiliation":[{"name":"FDNA Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Fleischer","sequence":"additional","affiliation":[{"name":"FDNA Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Kamphans","sequence":"additional","affiliation":[{"name":"GeneTalk Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Mundlos","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaron","family":"Gurovich","sequence":"additional","affiliation":[{"name":"FDNA Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-8625","authenticated-orcid":false,"given":"Peter M.","family":"Krawitz","sequence":"additional","affiliation":[{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin, Humboldt\u2010Universit\u00e4t zu Berlin, and Berlin Institute of Health Berlin Germany"},{"name":"Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn Rheinische Friedrich\u2010Wilhelms\u2010Universit\u00e4t Bonn Bonn Germany"},{"name":"GeneTalk Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,5]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10545-005-0112-z"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ejhg.5201673"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"BradleyLA HaddowHRM PalomakiGE(2017)Treatment of mucopolysaccharidosis type II (hunter syndrome): results from a systematic evidence review.Genet Med.10.1038\/gim.2017.30","DOI":"10.1038\/gim.2017.30"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-4439(98)00046-5"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.02020"},{"key":"e_1_2_8_7_1","unstructured":"GurovichY HananiY BarOet al (2018)DeepGestalt \u2014 identifying rare genetic syndromes using deep learning. 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