{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T21:32:07Z","timestamp":1790026327417,"version":"4.0.1"},"reference-count":19,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acad Dermatol Venereol"],"published-print":{"date-parts":[[2024,3]]},"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The assessment of hidradenitis suppurativa (HS) severity requires detailed, and error\u2010prone lesion counts. This proof\u2010of\u2010concept study aimed to automatically classify HS disease severity using machine learning of clinical smartphone images.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n 777 ambient\u2010light and size\u2010controlled images were used to build a class\u2010balanced synthetic dataset (\n n<\/jats:italic>\n \u2009=\u20097675). Convolutional neural networks (CNN) were used for automated severity classification (scale 0\u20133), and to assess disease\u2010dynamics. International Hidradenitis Suppurativa Severity Score System (IHS4) served as reference. A U\u2010NET algorithm was implemented for automated localization of diseased skin.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n CNNs were able to distinguish no\/mild from moderate\/severe disease with an overall prediction accuracy of 78% [receiver operating curve (AUC) 0.85]. Correct IHS4 classification was achieved with an overall accuracy of 72% (AUC 0.84\u20130.89). In addition, disease dynamics using IHS4 numerical values aligned with CNN outputs (NRMSE 0.262). The UNET algorithm localized lesions with a pixel accuracy of 88.1% and test loss of 0.42.<\/jats:p>\n <\/jats:sec>\n \n Limitations<\/jats:title>\n Limitations in assessing tattooed and hairy skin. Limited number of patients with dark skin colour and Hurley I.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n CNNs were able to distinguish no\/mild from moderate\/severe disease, classify disease severity over time, and automatically identify diseased skin areas and the skin phototype. This study breaks new grounds for fast, reliable, reproducible and easy\u2010to\u2010use HS severity assessments using clinical images.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/jdv.19639","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T02:57:55Z","timestamp":1701140275000},"page":"576-582","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated classification of hidradenitis suppurativa disease severity by convolutional neural network analyses using calibrated clinical images"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1029-2302","authenticated-orcid":false,"given":"A.","family":"Wiala","sequence":"first","affiliation":[{"name":"Department of Dermatology Clinic Landstrasse Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Ranjan","sequence":"additional","affiliation":[{"name":"SCARLETRED Holding GmbH Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Schnidar","sequence":"additional","affiliation":[{"name":"SCARLETRED Holding GmbH Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Rappersberger","sequence":"additional","affiliation":[{"name":"Department of Dermatology Clinic Landstrasse Vienna Austria"},{"name":"School of Medicine Sigmund Freud University Vienna Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0296-3567","authenticated-orcid":false,"given":"C.","family":"Posch","sequence":"additional","affiliation":[{"name":"School of Medicine Sigmund Freud University Vienna Austria"},{"name":"Department of Dermatology Clinic Hietzing Vienna Austria"},{"name":"Department of Dermatology and Allergy, School of Medicine, German Cancer Consortium (DKTK) Technical University of Munich Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2015.07.052"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2019.08.090"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365\u20102133.2001.04137.x"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2015.07.054"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.14475"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.13216"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.15748"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2018.1599"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010018\u20100300\u20107"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010022\u201001772\u20109"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1159\/000493342"},{"key":"e_1_2_10_13_1","first-page":"363","article-title":"128 SHADES OF RED: objective remote assessment of radiation dermatitis by augmented digital skin imaging","volume":"236","author":"Partl R","year":"2017","journal-title":"Stud Health Technol Inform"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104952"},{"key":"e_1_2_10_15_1","unstructured":"Schnidar HaraldAN.Methods for assessing erythema. Patent EP2976013A12016."},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/dth.15869"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.15163"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.17716"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.18983"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1159\/000501771"}],"container-title":["Journal of the European Academy of Dermatology and Venereology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jdv.19639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T06:37:50Z","timestamp":1708929470000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jdv.19639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"references-count":19,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1111\/jdv.19639"],"URL":"https:\/\/doi.org\/10.1111\/jdv.19639","archive":["Portico"],"relation":{},"ISSN":["0926-9959","1468-3083"],"issn-type":[{"value":"0926-9959","type":"print"},{"value":"1468-3083","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"2023-08-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}