{"title":"Facial features of lysosomal storage disorders.","authors":"Andrea D'Souza, Emory Ryan, Ellen Sidransky","doi":"10.1080/17446651.2022.2144229","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The use of facial recognition technology has diversified the diagnostic toolbelt for clinicians and researchers for the accurate diagnoses of patients with rare and challenging disorders. Specific identifiers in patient images can be grouped using artificial intelligence to allow the recognition of diseases and syndromes with similar features. Lysosomal storage disorders are rare, and some have prominent and unique features that may be used to train the accuracy of facial recognition software algorithms. Noteworthy features of lysosomal storage disorders (LSDs) include facial features such as prominent brows, wide noses, thickened lips, mouth, and chin, resulting in coarse and rounded facial features.</p><p><strong>Areas covered: </strong>We evaluated and report the prevalence of facial phenotypes in patients with different LSDs, noting two current examples when artificial intelligence strategies have been utilized to identify distinctive facies.</p><p><strong>Expert opinion: </strong>Specific LSDs, including Gaucher disease, Mucolipidosis IV and Fabry disease have recently been distinguished using facial recognition software. Additional lysosomal disorders LSDs lysosomal storage disorders with unique and distinguishable facial features also merit evaluation using this technology. These tools may ultimately aid in the identification of specific LSDs and shorten the diagnostic odyssey for patients with these rare and under-recognized disorders.</p>","PeriodicalId":12107,"journal":{"name":"Expert Review of Endocrinology & Metabolism","volume":"17 6","pages":"467-474"},"PeriodicalIF":2.7000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817214/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Endocrinology & Metabolism","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17446651.2022.2144229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/11/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: The use of facial recognition technology has diversified the diagnostic toolbelt for clinicians and researchers for the accurate diagnoses of patients with rare and challenging disorders. Specific identifiers in patient images can be grouped using artificial intelligence to allow the recognition of diseases and syndromes with similar features. Lysosomal storage disorders are rare, and some have prominent and unique features that may be used to train the accuracy of facial recognition software algorithms. Noteworthy features of lysosomal storage disorders (LSDs) include facial features such as prominent brows, wide noses, thickened lips, mouth, and chin, resulting in coarse and rounded facial features.
Areas covered: We evaluated and report the prevalence of facial phenotypes in patients with different LSDs, noting two current examples when artificial intelligence strategies have been utilized to identify distinctive facies.
Expert opinion: Specific LSDs, including Gaucher disease, Mucolipidosis IV and Fabry disease have recently been distinguished using facial recognition software. Additional lysosomal disorders LSDs lysosomal storage disorders with unique and distinguishable facial features also merit evaluation using this technology. These tools may ultimately aid in the identification of specific LSDs and shorten the diagnostic odyssey for patients with these rare and under-recognized disorders.
期刊介绍:
Implicated in a plethora of regulatory dysfunctions involving growth and development, metabolism, electrolyte balances and reproduction, endocrine disruption is one of the highest priority research topics in the world. As a result, we are now in a position to better detect, characterize and overcome the damage mediated by adverse interaction with the endocrine system. Expert Review of Endocrinology and Metabolism (ISSN 1744-6651), provides extensive coverage of state-of-the-art research and clinical advancements in the field of endocrine control and metabolism, with a focus on screening, prevention, diagnostics, existing and novel therapeutics, as well as related molecular genetics, pathophysiology and epidemiology.