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Sökning: WFRF:(Tschandl Philipp) > (2019) > Expert-Level Diagno...

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FältnamnIndikatorerMetadata
00004769naa a2200577 4500
001oai:gup.ub.gu.se/274567
003SwePub
008240528s2019 | |||||||||||000 ||eng|
009oai:prod.swepub.kib.ki.se:140017791
0247 a https://gup.ub.gu.se/publication/2745672 URI
0247 a https://doi.org/10.1001/jamadermatol.2018.43782 DOI
0247 a http://kipublications.ki.se/Default.aspx?queryparsed=id:1400177912 URI
040 a (SwePub)gud (SwePub)ki
041 a eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Tschandl, Philipp4 aut
2451 0a Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
264 1b American Medical Association (AMA),c 2019
520 a Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P<.001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P=.001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P=.18).Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Dermatologi och venereologi0 (SwePub)302042 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Dermatology and Venereal Diseases0 (SwePub)302042 hsv//eng
7001 a Rosendahl, Cliff4 aut
7001 a Akay, Bengu Nisa4 aut
7001 a Argenziano, Giuseppe4 aut
7001 a Blum, Andreas4 aut
7001 a Braun, Ralph P4 aut
7001 a Cabo, Horacio4 aut
7001 a Gourhant, Jean-Yves4 aut
7001 a Kreusch, Jürgen4 aut
7001 a Lallas, Aimilios4 aut
7001 a Lapins, Janu Karolinska Institutet4 aut
7001 a Marghoob, Ashfaq4 aut
7001 a Menzies, Scott4 aut
7001 a Neuber, Nina Maria4 aut
7001 a Paoli, John,d 1975u Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology4 aut0 (Swepub:gu)xpaojo
7001 a Rabinovitz, Harold S4 aut
7001 a Rinner, Christoph4 aut
7001 a Scope, Alon4 aut
7001 a Soyer, H Peter4 aut
7001 a Sinz, Christoph4 aut
7001 a Thomas, Luc4 aut
7001 a Zalaudek, Iris4 aut
7001 a Kittler, Harald4 aut
7102 a Karolinska Institutetb Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi4 org
7730 t JAMA dermatologyd : American Medical Association (AMA)g 55:1, s. 58-65q 55:1<58-65x 2168-6084x 2168-6068
8564 u https://jamanetwork.com/journals/jamadermatology/articlepdf/2716294/jamadermatology_tschandl_2018_oi_180064.pdf
8564 8u https://gup.ub.gu.se/publication/274567
8564 8u https://doi.org/10.1001/jamadermatol.2018.4378
8564 8u http://kipublications.ki.se/Default.aspx?queryparsed=id:140017791

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