{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T20:25:09Z","timestamp":1789244709531,"version":"build-2803163510"},"reference-count":21,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100016015","name":"Universit\u00e4tsspital Basel","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acad Dermatol Venereol"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Deep\u2010learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real\u2010world dermoscopic image transformations affect CNN robustness.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN\u20101\/CNN\u20102). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (<jats:italic>n<\/jats:italic>\u2009=\u20093).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN\u20101 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN\u20101: 49.5(\u00b134.3)%; CNN\u20102: 71.4(\u00b122.5)%; <jats:italic>p<\/jats:italic>\u2009=\u20090.03), while CNN\u20102 outperformed for clinically benign lesions with benign scoring (mvc: CNN\u20101: 49.7(\u00b122.7)%; CNN\u20102: 23.8(\u00b129.3)%; <jats:italic>p<\/jats:italic>\u2009=\u20090.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN\u20101: 94%; CNN\u20102: 89%). Intraclass correlation coefficients indicated \u2018moderate\u2019\u2010to\u2010\u2018good\u2019 reliability for both systems (CNN\u20101: 0.80, 95% CI:0.71\u20130.87, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001; CNN\u20102: 0.67, 95% CI:0.55\u20130.77, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Potential user\u2010induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross\u2010validation with repeated image sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Trial Registration<\/jats:title><jats:p><jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/clinicaltrials.gov\">ClinicalTrials.gov<\/jats:ext-link> (NCT04605822).<\/jats:p><\/jats:sec>","DOI":"10.1111\/jdv.19777","type":"journal-article","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T03:35:36Z","timestamp":1703907336000},"page":"945-953","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real\u2010world study about the pitfalls of augmented intelligence"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9995-2912","authenticated-orcid":false,"given":"E. 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