{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T23:52:01Z","timestamp":1794009121829,"version":"4.8.0"},"reference-count":43,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jdsjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Dermatological Science"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1016\/j.jdermsci.2020.11.009","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T11:56:06Z","timestamp":1606910166000},"page":"115-122","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":40,"title":["A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi"],"prefix":"10.1016","volume":"101","author":[{"given":"Linda","family":"Tognetti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Bonechi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Andreini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Bianchini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franco","family":"Scarselli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Cevenini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elvira","family":"Moscarella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Farnetani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caterina","family":"Longo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimilios","family":"Lallas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Carrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susana","family":"Puig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danica","family":"Tiodorovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean Luc","family":"Perrot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Pellacani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Argenziano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisa","family":"Cinotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gennaro","family":"Cataldo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Balistreri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Mecocci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Gori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Rubegni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra","family":"Cartocci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10151","key":"10.1016\/j.jdermsci.2020.11.009_bib0005","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/S0140-6736(18)31559-9","article-title":"Melanoma","volume":"392","author":"Schadendorf","year":"2018","journal-title":"The Lancet"},{"issue":"7","key":"10.1016\/j.jdermsci.2020.11.009_bib0010","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1111\/jdv.14129","article-title":"Eurodermoscopy working group; Argenziano G, geller AC. The impact of dermoscopy on melanoma detection in the practice of dermatologists in Europe: results of a pan-European survey","volume":"31","author":"Forsea","year":"2017","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"issue":"12","key":"10.1016\/j.jdermsci.2020.11.009_bib0015","article-title":"Cochrane skin cancer diagnostic test accuracy group. Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults","volume":"12","author":"Dinnes","year":"2018","journal-title":"Cochrane Database Syst Rev"},{"issue":"12","key":"10.1016\/j.jdermsci.2020.11.009_bib0020","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1111\/jdv.15106","article-title":"An integrated clinical-dermoscopic risk scoring system for the differentiation between early melanoma and atypical nevi: the iDScore","volume":"32","author":"Tognetti","year":"2018","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"issue":"4","key":"10.1016\/j.jdermsci.2020.11.009_bib0025","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1111\/j.1365-2133.2005.06481.x","article-title":"Dysplastic naevus vs. In situ melanoma: digital dermoscopy analysis","volume":"152","author":"Burroni","year":"2005","journal-title":"Br. J. Dermatol."},{"issue":"11","key":"10.1016\/j.jdermsci.2020.11.009_bib0030","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1001\/archderm.141.11.1444","article-title":"The role of dermoscopy and digital dermoscopy analysis in the diagnosis of pigmented skin lesions","volume":"141","author":"Rubegni","year":"2005","journal-title":"Arch. Dermatol."},{"issue":"6","key":"10.1016\/j.jdermsci.2020.11.009_bib0035","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1097\/CMR.0000000000000209","article-title":"Computer-assisted melanoma diagnosis: a new integrated system","volume":"25","author":"Rubegni","year":"2015","journal-title":"Melanoma Res."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0040","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0045","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0050","series-title":"A Two Stage Gan for High Resolution Retinal Image Generation and Segmentation","author":"Andreini","year":"2019"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0055","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.3390\/app9112192","article-title":"Confidence measures for deep learning in domain adaptation","volume":"9","author":"Bonechi","year":"2019","journal-title":"Appl Sci"},{"issue":"2","key":"10.1016\/j.jdermsci.2020.11.009_bib0060","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jaad.2017.08.016","article-title":"International skin imaging collaboration. Results of the 2016 International skin imaging collaboration ISBI challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images","volume":"78","author":"Marchetti","year":"2018","journal-title":"J. Am. Acad. Dermatol."},{"issue":"7639","key":"10.1016\/j.jdermsci.2020.11.009_bib0065","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.jdermsci.2020.11.009_bib0070","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1016\/j.jid.2018.01.028","article-title":"Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm","volume":"138","author":"Han","year":"2018","journal-title":"J. Invest. Dermatol."},{"issue":"2","key":"10.1016\/j.jdermsci.2020.11.009_bib0075","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1111\/bjd.16924","article-title":"Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis","volume":"180","author":"Fujisawa","year":"2019","journal-title":"Br. J. Dermatol."},{"issue":"8","key":"10.1016\/j.jdermsci.2020.11.009_bib0080","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","article-title":"Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists","volume":"29","author":"Haenssle","year":"2018","journal-title":"Ann. Oncol."},{"issue":"6","key":"10.1016\/j.jdermsci.2020.11.009_bib0085","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1111\/jdv.16165","article-title":"Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas","volume":"34","author":"Fink","year":"2020","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0090","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ejca.2019.11.020","article-title":"Melanoma recognition by a deep learning convolutional neural network-performance in different melanoma subtypes and localisations","volume":"127","author":"Winkler","year":"2020","journal-title":"Eur. J. Cancer"},{"issue":"8","key":"10.1016\/j.jdermsci.2020.11.009_bib0095","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1111\/jdv.16185","article-title":"Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks","volume":"34","author":"Lee","year":"2020","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"issue":"1","key":"10.1016\/j.jdermsci.2020.11.009_bib0100","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1001\/jamadermatol.2018.4378","article-title":"Expert level diagnosis of nonpigmented skin cancer by combined convolutional neural networks","volume":"155","author":"Tschandl","year":"2019","journal-title":"JAMA Dermatol"},{"issue":"10","key":"10.1016\/j.jdermsci.2020.11.009_bib0105","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2019.13436","article-title":"Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions","volume":"2","author":"Phillips","year":"2019","journal-title":"JAMA Netw Open"},{"issue":"1","key":"10.1016\/j.jdermsci.2020.11.009_bib0110","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.annonc.2019.10.013","volume":"31","author":"Haenssle","year":"2020","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0115","series-title":"Technology in Practical Dermatology, Photorejuvenation: Concepts, Practice, Perspectives","first-page":"3","article-title":"Dermoscopy: fundamentals and technology advances","author":"Tognetti","year":"2020"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0120","series-title":"New Trends in Image Analysis and Processing","first-page":"211","article-title":"Fusion of visual and anamnestic data for the classification of skin lesions with deep learning","author":"Bonechi","year":"2019"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0125","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"168","article-title":"Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)","author":"Codella","year":"2018"},{"issue":"3","key":"10.1016\/j.jdermsci.2020.11.009_bib0130","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1111\/jdv.15923","article-title":"Validation of an integrated dermoscopic scoring method in an European teledermoscopy web platform: the iDScore project for early detection of melanoma","volume":"34","author":"Tognetti","year":"2020","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0135","series-title":"NCCN Clinical Practice Guidelines in Oncology: Melanoma. Version 1.2020 \u2014 December 19","author":"National Comprehensive Cancer Network","year":"2019"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0140","first-page":"09026","article-title":"Weak supervision for generating pixel-level annotations in scene text segmentation","volume":"1911","author":"Bonechi","year":"2019","journal-title":"arXiv preprint arXiv"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0145","series-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","author":"Abadi","year":"2015"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0150","series-title":"MICCAI 2015. Lecture Notes in Computer Science","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"3","key":"10.1016\/j.jdermsci.2020.11.009_bib0155","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing areas under two or more correlated receiver operating characteristics curves: a nonparamentric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"issue":"1","key":"10.1016\/j.jdermsci.2020.11.009_bib0160","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"issue":"9","key":"10.1016\/j.jdermsci.2020.11.009_bib0165","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1056\/NEJMp1912079","article-title":"Transforming global health with AI","volume":"382","author":"Mehta","year":"2020","journal-title":"N. Engl. J. Med."},{"issue":"10","key":"10.1016\/j.jdermsci.2020.11.009_bib0170","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1001\/jamadermatol.2019.1633","article-title":"Marking the path toward artificial intelligence-based image classification in dermatology","volume":"155","author":"Novoa","year":"2019","journal-title":"JAMA Dermatol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0175","doi-asserted-by":"crossref","first-page":"N1176","DOI":"10.1016\/j.jaad.2019.06.042","article-title":"Assessing the effectiveness of artificial intelligence methods for melanoma: a retrospective review","volume":"81","author":"Cui","year":"2019","journal-title":"J. Am. Acad. Dermatol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0180","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","article-title":"Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task","volume":"113","author":"Brinker","year":"2019","journal-title":"Eur J Canc"},{"issue":"7","key":"10.1016\/j.jdermsci.2020.11.009_bib0185","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","article-title":"Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study","volume":"20","author":"Tschandl","year":"2019","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0190","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.ejca.2019.07.019","article-title":"Superior skin cancer classification by the combination of human and artificial intelligence","volume":"120","author":"Hekler","year":"2019","journal-title":"Eur J Canc"},{"issue":"2","key":"10.1016\/j.jdermsci.2020.11.009_bib0195","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/0272989X03251246","article-title":"The area under an ROC curve with limited information","volume":"23","author":"Hout WB","year":"2003","journal-title":"Med. Decis. Making"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0200","first-page":"219","article-title":"Area of the ROC curve when one point is available","volume":"190","author":"Katostaras","year":"2013","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0205","unstructured":"L. Tognetti, A. Cartocci, E. Cinotti, et al. The impact of anatomical location and sun exposure on the dermoscopic recognition of atypical nevi and early melanomas: usefulness of an integrated clinical-dermoscopic method (iDScore) [published online ahead of print, 2020 Aug 2]."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0210","unstructured":"L. Tognetti, A. Cartocci, A. Balistreri, et al. The Comparative Use of Multiple Electronic Devices in the Teledermoscopic Diagnosis of Early Melanoma [published online ahead of print, 2020 Aug 14]. Telemed J E Health. 2020;10. h."},{"key":"10.1016\/j.jdermsci.2020.11.009_bib0215","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human\u2013computer collaboration for skin cancer recognition","volume":"26","author":"Tschandl","year":"2020","journal-title":"Nat. Med."}],"container-title":["Journal of Dermatological Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923181120303947?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923181120303947?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:51:01Z","timestamp":1760331061000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0923181120303947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["S0923181120303947"],"URL":"https:\/\/doi.org\/10.1016\/j.jdermsci.2020.11.009","relation":{},"ISSN":["0923-1811"],"issn-type":[{"value":"0923-1811","type":"print"}],"subject":[],"published":{"date-parts":[[2021,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi","name":"articletitle","label":"Article Title"},{"value":"Journal of Dermatological Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jdermsci.2020.11.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}