{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:41:45Z","timestamp":1792010505099,"version":"4.4.0"},"reference-count":28,"publisher":"Medical Journals Sweden AB","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Acta Derm Venereol"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 independent dermatologists. The secondary aim was to address which clinical and dermoscopic features dermatologists found to be suggestive of invasive and in situ melanomas, respectively. A retrospective investigation was conducted including 1,578 cases of paired images of invasive (n\u2009=\u2009728, 46.1%) and in situ melanomas (n\u2009=\u2009850, 53.9%). All images were obtained from the Department of Dermatology and Venereology at Sahlgrenska University Hospital and were randomized to a training set (n\u2009=\u20091,078), a validation set (n\u2009=\u2009200) and a test set (n\u2009=\u2009300). The area under the receiver operating characteristics curve (AUC) among the dermatologists ranged from 0.75 (95% confidence interval 0.70\u20130.81) to 0.80 (95% confidence interval 0.75\u20130.85). The combined dermatologists\u2019 AUC was 0.80 (95% confidence interval 0.77\u20130.86), which was significantly higher than the CNN model (0.73, 95% confidence interval 0.67\u20130.78, p\u2009=\u20090.001). Three of the dermatologists significantly outperformed the CNN. Shiny white lines, atypical blue-white structures and polymorphous vessels displayed a moderate interobserver agreement, and these features also correlated with invasive melanoma. Prospective trials are needed to address the clinical usefulness of CNN models in this setting.<\/jats:p>","DOI":"10.2340\/actadv.v102.2681","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T06:51:31Z","timestamp":1664434291000},"page":"adv00790","source":"Crossref","is-referenced-by-count":10,"title":["Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network"],"prefix":"10.2340","volume":"102","author":[{"given":"Martin","family":"Gillstedt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ludwig","family":"Mannius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Paoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Dahl\u00e9n Gyllencreutz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Fougelberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Johansson Backman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenna","family":"Pakka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Zaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sam","family":"Polesie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1183","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"22262","doi-asserted-by":"crossref","unstructured":"Kovarik C, Lee I, Ko J, Ad Hoc Task force on augmented I. 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