{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T08:02:44Z","timestamp":1784880164920,"version":"3.55.0"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100008005","name":"Yonsei University College of Medicine","doi-asserted-by":"publisher","award":["2024\u201032\u20100099"],"award-info":[{"award-number":["2024\u201032\u20100099"]}],"id":[{"id":"10.13039\/501100008005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Deutsche Derma Gesell"],"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>Nail unit melanoma (NUM) is a rare but potentially fatal malignancy often misdiagnosed as melanonychia. Because biopsy may cause permanent nail dystrophy, accurate noninvasive diagnosis is essential. This study aimed to develop and validate an artificial intelligence model to distinguish NUM from benign melanonychia using clinical images and to assess its diagnostic utility through human comparison and external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>Clinical images from 172 patients with melanonychia and 122 patients with NUM were retrospectively collected. Pediatric melanonychia was excluded to minimize diagnostic ambiguity. Three convolutional neural networks (CNNs) were trained and validated with patient\u2010wise splits. Human validation involved 40 representative images assessed by dermatologists, residents, and non\u2010medical participants, with and without CNN assistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ResNet\u201050 achieved the highest sensitivity (87.2 %). DenseNet\u2010121 achieved the highest AUROC (area under the receiver operating characteristic curve) (0.954). CNN assistance improved the diagnostic accuracy of all human raters (accuracy increased from 70.0 % to 80.8 %), with dermatology residents showing the largest gain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed CNN\u2010based models demonstrated robust performance in differentiating NUM from melanonychia and improved diagnostic accuracy and agreement among human evaluators. While not intended to replace clinical judgment, this approach shows promise as a supportive screening tool in clinical settings, warranting further validation in larger, multi\u2010institutional, and multi\u2010ethnic cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ddg.70347","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T07:20:36Z","timestamp":1784877636000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI\u2010assisted diagnosis of nail unit melanoma and melanonychia using a clinical deep learning model"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0930-4628","authenticated-orcid":false,"given":"Yusung","family":"Chu","sequence":"first","affiliation":[{"name":"Department of Precision Medicine Yonsei University Wonju College of Medicine  Wonju\u2010si Gangwon\u2010do Republic of 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Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon\u2010Goon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Dermatology Yeungnam University College of Medicine  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2103-8297","authenticated-orcid":false,"given":"Hae June","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Dermatology Severance Hospital Cutaneous Biology Research Institute Yonsei University College of Medicine  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9575-5665","authenticated-orcid":false,"given":"Byungho","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Dermatology Severance Hospital Cutaneous Biology Research Institute Yonsei University College of Medicine  Seoul South 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