{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:07:56Z","timestamp":1790885276725,"version":"4.1.0"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006506","name":"Minist\u00e9rio da Sa\u00fade","doi-asserted-by":"publisher","award":["25000.121674\/2018-13"],"award-info":[{"award-number":["25000.121674\/2018-13"]}],"id":[{"id":"10.13039\/501100006506","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists\u2019 diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and\/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.<\/jats:p>","DOI":"10.1371\/journal.pone.0257006","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T06:42:14Z","timestamp":1632724934000},"page":"e0257006","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":35,"title":["Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7059-4068","authenticated-orcid":true,"given":"Mara","family":"Giavina-Bianchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0934-5852","authenticated-orcid":true,"given":"Raquel Machado","family":"de 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SAPS, Servi\u00e7o de Aten\u00e7\u00e3o Prim\u00e1ria \u00e0 Sa\u00fade 2021 [Available from: https:\/\/aps.saude.gov.br\/noticia\/10136#:~:text=Atualmente%2C%20o%20Brasil%20tem%20mais,Aten%C3%A7%C3%A3o%20Prim%C3%A1ria%20atuando%20no%20territ%C3%B3rio."},{"issue":"6","key":"pone.0257006.ref019","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1590\/abd1806-4841.20143276","article-title":"Distribution of Brazilian dermatologists according to geographic location, population and HDI of municipalities: an ecological study","volume":"89","author":"JV Schmitt","year":"2014","journal-title":"An Bras Dermatol"},{"key":"pone.0257006.ref020","doi-asserted-by":"crossref","first-page":"100641","DOI":"10.1016\/j.eclinm.2020.100641","article-title":"Teledermatology reduces dermatology referrals and improves access to specialists","volume":"29\u201330","author":"M Giavina-Bianchi","year":"2020","journal-title":"EClinicalMedicine"},{"key":"pone.0257006.ref021","doi-asserted-by":"crossref","unstructured":"Codella NCF, Gutman D, Celebi ME. Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI). 2017.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"pone.0257006.ref022","unstructured":"Combalia C, Codella NCF, Rotemberg V. BCN20000: Dermoscopic Lesions in the Wild. 2019."},{"key":"pone.0257006.ref023"},{"key":"pone.0257006.ref024","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. Analyzing and improving the image quality of stylegan. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition2020. p. 8110\u20139.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"pone.0257006.ref025","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Grishick R. Mask r-cnn. Proceedings of the IEEE international conference on computer vision 2017. p. 2961\u20139.","DOI":"10.1109\/ICCV.2017.322"},{"key":"pone.0257006.ref026","unstructured":"Brooks J. COCO Annotator 2020 [Available from: https:\/\/github.com\/jsbroks\/coco-annotator\/."},{"key":"pone.0257006.ref027","unstructured":"Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks. International Conference on Machine Learning2019. p. 6105\u201314."},{"key":"pone.0257006.ref028","unstructured":"Lin T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision 2017. p. 2980\u20138."},{"key":"pone.0257006.ref029","unstructured":"ISIC. ISIC challenge. 2019."},{"key":"pone.0257006.ref030","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. p. 1\u201314."},{"key":"pone.0257006.ref031","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J. Facenet: a unified embedding for face recognition and clustering. Proceedings of the IEEE conference on computer vision and pattern recognition 2015. p. 815\u201323.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"pone.0257006.ref032","unstructured":"Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T and Keutzer K. DenseNet: Implementing Efficient ConvNet Descriptor Pyramids. Proceedings of the IEEE conference on computer vision and pattern recognition 2014. p. 892\u201390."},{"key":"pone.0257006.ref033","unstructured":"RR S, CogswellM, A D, R V, D P, D B. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. 2019."},{"key":"pone.0257006.ref034","article-title":"Human-computer collaboration for skin cancer recognition","author":"P Tschandl","year":"2020","journal-title":"Nat Med"},{"key":"pone.0257006.ref035","doi-asserted-by":"crossref","unstructured":"Pooch EH, Ballester P, Barros RC. Can We Trust Deep Learning Based Diagnosis? The Impact of Domain Shift in Chest Radiograph Classification. 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