Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma.Methods: In this cross-s...
| Publicado en: | Journal of the American Academy of Dermatology Vol. 82; no. 3; pp. 622 - 628 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141682408&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141682408 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01909622 1ZF jtl: Journal of the American Academy of Dermatology issn: 01909622 maglogo: N pubinfo: dt: Mar2020 vid: 82 iid: 3 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 141682408 141682408 NLM31306724 141682408 10.1016/j.jaad.2019.07.016 NLM31306724 141682408 ppf: 622 ppct: 6 formats: tig: atl: Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017. aug: au: Marchetti, Michael A. Liopyris, Konstantinos Dusza, Stephen W. Codella, Noel C.F. Gutman, David A. Helba, Brian Kalloo, Aadi Halpern, Allan C. affil: Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York sug: subj: Melanoma Diagnosis Microscopy Methods Image Interpretation, Computer Assisted Methods Skin Neoplasms Diagnosis Microscopy Statistics and Numerical Data Colombia United States Skin Pathology Skin Neoplasms Pathology Skin Cross Sectional Studies ROC Curve Melanoma Pathology International Relations Nevus Diagnosis Israel Spain Internship and Residency Statistics and Numerical Data Diagnosis, Differential Keratosis Diagnosis Funding Source Human ab: Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma.Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level.Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%.Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata.Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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