{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T04:24:00Z","timestamp":1790223840391,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acad Dermatol Venereol"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To assess the performance of image\u2010based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image\u2010based scoring by physicians.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Imaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI\u2010trained physicians retrospectively determined image\u2010based subscores on the test set images of the trunk. Agreement with the real\u2010life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Intraclass correlation coefficients between the CNN and real\u2010life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI\u2010trained physicians (<jats:italic>N<\/jats:italic>\u2009=\u20095) were in moderate\u2013good agreement (ICCs 0.706\u20130.793) with each other for image\u2010based PASI scoring of the trunk region. ICCs between the CNN and real\u2010life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image\u2010based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Convolutional Neural Networks have the potential to automatically and objectively perform image\u2010based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image\u2010based PASI scoring.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jdv.17711","type":"journal-article","created":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T23:36:33Z","timestamp":1634340993000},"page":"68-75","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Image\u2010based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7887-6737","authenticated-orcid":false,"given":"M.J.","family":"Schaap","sequence":"first","affiliation":[{"name":"Department of Dermatology Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.J.","family":"Cardozo","sequence":"additional","affiliation":[{"name":"Department of Dermatology Radboud University Medical Center Nijmegen The Netherlands"},{"name":"Department of Medical Imaging Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-4744","authenticated-orcid":false,"given":"A.","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3872-5704","authenticated-orcid":false,"given":"E.M.G.J.","family":"de Jong","sequence":"additional","affiliation":[{"name":"Department of Dermatology Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2028-8972","authenticated-orcid":false,"given":"B.","family":"van Ginneken","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-8294","authenticated-orcid":false,"given":"M.M.B.","family":"Seyger","sequence":"additional","affiliation":[{"name":"Department of Dermatology Radboud University Medical Center Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/jid.2012.339"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1159\/000250839"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/ajd.12280"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.14960"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2007.4353072"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/07434610902744074"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2230.2008.03169.x"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-018-1110-7"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.17200"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.3109\/09546634.2011.596184"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35101-3_7"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2236349"},{"key":"e_1_2_6_14_1","first-page":"2380","volume-title":"Conference Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Lu J","year":"2013"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1142\/9789814611107_0007"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEPE.2014.6969867"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2910883"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09825-6"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7899846"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01201-4_27"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.11.008"},{"key":"e_1_2_6_22_1","first-page":"770","article-title":"Deep residual learning for image recognition","volume":"2016","author":"He K","year":"2016","journal-title":"IEEE Conf Comput Vision Pattern Recognit"},{"key":"e_1_2_6_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_2_6_24_1","article-title":"A method for stochastic optimization","author":"Kingma DP","year":"2015","journal-title":"CoRR"},{"key":"e_1_2_6_25_1","article-title":"PyTorch: An imperative style, high\u2010performance deep learning library","author":"Paszke A","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_6_26_1","unstructured":"MeakinJ ZeelandH KoekM GerkePK deDobbelaerB PinckaersHet\u00a0al.Grand Challenge.org. Version v2020.12 ed. Zenodo2020."},{"key":"e_1_2_6_27_1","first-page":"4920","article-title":"Ordinal regression with multiple output CNN for age estimation","volume":"2016","author":"Niu Z","year":"2016","journal-title":"IEEE Conf Comput Vision Pattern Recognit"},{"key":"e_1_2_6_28_1","doi-asserted-by":"publisher","DOI":"10.2466\/pr0.1966.19.1.3"},{"key":"e_1_2_6_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00403-020-02145-3"}],"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.17711","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/jdv.17711","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jdv.17711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T08:31:22Z","timestamp":1693211482000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jdv.17711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1111\/jdv.17711"],"URL":"https:\/\/doi.org\/10.1111\/jdv.17711","archive":["Portico"],"relation":{},"ISSN":["0926-9959","1468-3083"],"issn-type":[{"value":"0926-9959","type":"print"},{"value":"1468-3083","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,18]]},"assertion":[{"value":"2021-06-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}