{"title":"即时使用面部分析技术的经验教训","authors":"Jon Soo Kim, Hansol Ko, Hyewon Woo, Won Seop Kim","doi":"10.26815/acn.2023.00227","DOIUrl":null,"url":null,"abstract":"Purpose: We aimed to evaluate the utility of facial analysis technology for genetic diagnoses in a typical pediatric genetic clinic. Methods: A retrospective review identified children (aged <18 years) who had not previously received a definitive genetic diagnosis and underwent a comprehensive genetic evaluation. Their photographs and relevant clinical non-facial features were uploaded to the CLINIC application of the Face2Gene web interface, and the resulting analysis was accessed and correlated to the molecular diagnosis. Results: Of the 23 children included, the overall diagnostic yield in this study was 60.9% (14/23). In total, 64.3% of patients had the correct condition suggested in the top 10 differential diagnoses. The gestalt similarity was only 55.6%, but the phenotypic features added by the clinician showed a similarity of more than the medium level in all patients. Conclusion: Our data underscore the usefulness of facial analysis technology as an auxiliary point-of-care tool in pediatric genetic clinics, and we also present some considerations to increase accuracy.","PeriodicalId":33305,"journal":{"name":"Annals of Child Neurology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lessons Learned from the Point-of-Care Use of a Facial Analysis Technology\",\"authors\":\"Jon Soo Kim, Hansol Ko, Hyewon Woo, Won Seop Kim\",\"doi\":\"10.26815/acn.2023.00227\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: We aimed to evaluate the utility of facial analysis technology for genetic diagnoses in a typical pediatric genetic clinic. Methods: A retrospective review identified children (aged <18 years) who had not previously received a definitive genetic diagnosis and underwent a comprehensive genetic evaluation. Their photographs and relevant clinical non-facial features were uploaded to the CLINIC application of the Face2Gene web interface, and the resulting analysis was accessed and correlated to the molecular diagnosis. Results: Of the 23 children included, the overall diagnostic yield in this study was 60.9% (14/23). In total, 64.3% of patients had the correct condition suggested in the top 10 differential diagnoses. The gestalt similarity was only 55.6%, but the phenotypic features added by the clinician showed a similarity of more than the medium level in all patients. Conclusion: Our data underscore the usefulness of facial analysis technology as an auxiliary point-of-care tool in pediatric genetic clinics, and we also present some considerations to increase accuracy.\",\"PeriodicalId\":33305,\"journal\":{\"name\":\"Annals of Child Neurology\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Child Neurology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26815/acn.2023.00227\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Child Neurology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26815/acn.2023.00227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Lessons Learned from the Point-of-Care Use of a Facial Analysis Technology
Purpose: We aimed to evaluate the utility of facial analysis technology for genetic diagnoses in a typical pediatric genetic clinic. Methods: A retrospective review identified children (aged <18 years) who had not previously received a definitive genetic diagnosis and underwent a comprehensive genetic evaluation. Their photographs and relevant clinical non-facial features were uploaded to the CLINIC application of the Face2Gene web interface, and the resulting analysis was accessed and correlated to the molecular diagnosis. Results: Of the 23 children included, the overall diagnostic yield in this study was 60.9% (14/23). In total, 64.3% of patients had the correct condition suggested in the top 10 differential diagnoses. The gestalt similarity was only 55.6%, but the phenotypic features added by the clinician showed a similarity of more than the medium level in all patients. Conclusion: Our data underscore the usefulness of facial analysis technology as an auxiliary point-of-care tool in pediatric genetic clinics, and we also present some considerations to increase accuracy.