{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T23:51:40Z","timestamp":1792021900535,"version":"4.4.0"},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2016,1,25]],"date-time":"2016-01-25T00:00:00Z","timestamp":1453680000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical Genetics"],"published-print":{"date-parts":[[2016,5]]},"abstract":"<jats:p>Facial analysis systems are becoming available to healthcare providers to aid in the recognition of dysmorphic phenotypes associated with a multitude of genetic syndromes. These technologies automatically detect facial points and extract various measurements from images to recognize dysmorphic features and evaluate similarities to known facial patterns (gestalts). To evaluate such systems' usefulness for supporting the clinical practice of healthcare professionals, the recognition accuracy of the Cornelia de Lange syndrome (<jats:styled-content style=\"fixed-case\">CdLS<\/jats:styled-content>) phenotype was examined with<jats:styled-content style=\"fixed-case\">FDNA<\/jats:styled-content>'s automated facial dysmorphology novel analysis (<jats:styled-content style=\"fixed-case\">FDNA<\/jats:styled-content>) technology. In the first experiment,<jats:styled-content style=\"fixed-case\">2D<\/jats:styled-content>facial images of<jats:styled-content style=\"fixed-case\">CdLS<\/jats:styled-content>patients with either an<jats:italic><jats:styled-content style=\"fixed-case\">NIPBL<\/jats:styled-content><\/jats:italic>or<jats:italic><jats:styled-content style=\"fixed-case\">SMC1A<\/jats:styled-content><\/jats:italic>gene mutation as well as non\u2010<jats:styled-content style=\"fixed-case\">CdLS<\/jats:styled-content>patients which were assessed by dysmorphologists in a previous study were evaluated by the<jats:styled-content style=\"fixed-case\">FDNA<\/jats:styled-content>technology; the average detection rate of experts was 77% while the system's detection rate was 87%. In the second study, when a new set of<jats:italic><jats:styled-content style=\"fixed-case\">NIPBL<\/jats:styled-content><\/jats:italic>,<jats:italic><jats:styled-content style=\"fixed-case\">SMC1A<\/jats:styled-content><\/jats:italic>and non\u2010<jats:styled-content style=\"fixed-case\">CdLS<\/jats:styled-content>patient photos was evaluated, the detection rate increased to 94%. The results from both studies indicated that the system's detection rate was comparable to that of dysmorphology experts. Therefore, utilizing such technologies may be a useful tool in a clinical setting.<\/jats:p>","DOI":"10.1111\/cge.12716","type":"journal-article","created":{"date-parts":[[2015,12,14]],"date-time":"2015-12-14T04:48:51Z","timestamp":1450068531000},"page":"557-563","source":"Crossref","is-referenced-by-count":84,"title":["Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis"],"prefix":"10.1111","volume":"89","author":[{"given":"L.","family":"Basel\u2010Vanagaite","sequence":"first","affiliation":[{"name":"Medical Genetics Department Schneider Children's Medical Center of Israel, Rabin Medical Center Petah Tikva Israel"},{"name":"Felsenstein Medical Research Center Petah Tikva Israel"},{"name":"Tel Aviv University Tel Aviv Israel"},{"name":"FDNA Inc. 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