[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-450712-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-450712-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","artificial-intelligence-assisted-prediction-of-demodex-mite-density-in-facial-erythema","Artificial intelligence-assisted prediction of Demodex mite density in facial erythema","","Artificial intelligence-assisted prediction of Demodex mite density in facial erythema supports more accurate, less operator-dependent assessment for dermatology. The study developed and evaluated a deep learning model (DemodexNet) using clinical data and photographs from 1,124 patients with facial erythema measured at two Korean referral hospitals from January 2016 to August 2023. DemodexNet reached AUC 0.823–0.865 internally and improved dermatologist diagnostic accuracy from 63.7% to 70.6%. Gains were larger for less-experienced clinicians with higher AI trust, and the model focused on central regions and characteristic lesions.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/artificial-intelligence-assisted-prediction-of-demodex-mite-density-in-facial-erythema/450712/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/artificial-intelligence-assisted-prediction-of-demodex-mite-density-in-facial-erythema/450712.png","ImageObject",300,407,{"name":42,"@type":43},"Jordan Avery","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-03","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does DemodexNet address in diagnosing demodicosis?","Question",{"text":63,"@type":64},"Current Demodex mite density detection methods are semi-invasive and depend heavily on operator proficiency. DemodexNet was developed to predict mite density from clinical data and photographs, reducing reliance on painful procedures and operator variability.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the dataset assembled for evaluating the model?",{"text":68,"@type":64},"The study included 1,124 patients with facial erythema who underwent Demodex mite density measurement at two referral hospitals in South Korea between January 2016 and August 2023.",{"name":70,"@type":61,"acceptedAnswer":71},"How did AI assistance affect dermatologists’ diagnostic performance?",{"text":72,"@type":64},"AI-assisted evaluation increased diagnostic accuracy among dermatologists from 63.7% to 70.6% (P \u003C .001). Less experienced dermatologists and those with higher trust in AI showed larger performance gains.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},450712,1790835290,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":113,"preview_page_count":4},1099523882367,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nArtificial intelligence-assisted prediction of Demodex mite density in facial erythema  \nJemin Kim1,7, Yun Na Lee2,7, Jihee Boo1, Inrok Oh3, Changyoon Lee4, Joo Hee Lee5, Ye Seul Choi1, Hyun Kim1, Jung Im Na6, Jihee Kim1􀀍 & ChangOok Park5􀀍  \nCurrent detection methods of Demodex mite density in facial erythema are semi-invasive or operatordependent. We developed and evaluated a deep learning model (DemodexNet) for predicting Demodex mite density and assessed its impact on the diagnostic performance of dermatologists. This study included 1,124 patients with facial erythema who underwent Demodex mite density measurement at two referral hospitals between January 2016 and August 2023. DemodexNet achieved area under the receiver operating characteristic curve values of 0.823–0.865 in internal testing, with lower values observed in the external testing set. AI-assisted evaluation was associated with an increase in diagnostic accuracy among dermatologists from 63.7% to 70.6%(P \u003C .001) . Less experienced dermatologists and those with higher trust in AI showed greater performance gains. The model recognized central facial regions and individual lesions characteristic of demodicosis. DemodexNet demonstrates promising performance in predicting Demodex mite density and significantly improves dermatologists’ diagnostic accuracy. As this proof-of-concept study was limited to Korean patients with Fitzpatrick skin types III-IV, validation in diverse populations is required before broader clinical application.  \nKeywords Facial erythema, Rosacea, Demodex mites, Artificial intelligence, Deep learning  \nFacial erythema, often referred to as “red face,” is a readily identifiable clinical manifestation in dermatology1. However, its presentation can result from diverse dermatological diseases and other medical conditions2. Rosacea is one of the most common chronic inflammatory conditions that present with frequent flushing and facial erythema. This condition can greatly affect the quality of life of patients and is associated with an increased risk of cardiovascular, gastrointestinal, mental, and neurological problems3. Although rosacea is the most emblematic disease associated with “red face,” the differential diagnosis encompasses a broad spectrum of conditions, including contact dermatitis, atopic dermatitis, seborrheic dermatitis, acne vulgaris, lupus erythematosus, and dermatomyositis4,5.  \nVarious genetic, environmental, and microbial determinants have been implicated in the etiology of facial erythema. Among these, Demodex mites, which reside within the pilosebaceous units of human facial skin as commensals, are significant contributors6. Demodex mites are commonly found in the skin of healthy adults, with a prevalence rate of 100% and a density of ≤ 5 mites/cm27. However, overproliferation of these mites can lead to pathogenic processes referred to as demodicosis, which result in various symptoms, including facial redness, irritation, itching, and inflammation8,9. The diagnosis of demodicosis is based on the evaluation of the number of Demodex mites present on the skin surface. This can involve a standardized skin surface biopsy or direct microscopic examination of fresh secretions from sebaceous glands (DME)10, 11. However, the utility of these methodologies in routine clinical practice is limited owing to their painful, semi-invasive nature and the significant impact of operator proficiency on the results10, 12.  \nThis study aimed to develop a deep learning model called DemodexNet, which can predict the density of Demodex mites by analyzing clinical data and photographs of patients with facial erythema. Furthermore,  \n1Department of Dermatology, Yongin Severance Hospital Yonsei University College of Medicine, Gyeonggi-do 16995, Yongin-si, Korea. 2Department of Transdisciplinary Medicine , Seoul National University Hospital , Seoul 03080, Korea. 3LG Chem Ltd , S","cbCailOS44WLJe5E","https://ap.wps.com/l/cbCailOS44WLJe5E","pdf",4160147,12,"English","# Methods\n## Study design and participant selection\n## Data preparation and preprocessing","[{\"question\":\"What problem does DemodexNet address in diagnosing demodicosis?\",\"answer\":\"Current Demodex mite density detection methods are semi-invasive and depend heavily on operator proficiency. DemodexNet was developed to predict mite density from clinical data and photographs, reducing reliance on painful procedures and operator variability.\"},{\"question\":\"How was the dataset assembled for evaluating the model?\",\"answer\":\"The study included 1,124 patients with facial erythema who underwent Demodex mite density measurement at two referral hospitals in South Korea between January 2016 and August 2023.\"},{\"question\":\"How did AI assistance affect dermatologists’ diagnostic performance?\",\"answer\":\"AI-assisted evaluation increased diagnostic accuracy among dermatologists from 63.7% to 70.6% (P \\u003c .001). Less experienced dermatologists and those with higher trust in AI showed larger performance gains.\"}]","Artificial intelligence-assisted prediction of Demodex mite density in facial erythema | PDF",1790733716]