Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.
Background: Computer vision may aid in melanoma detection.Objective: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.Methods: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi...
| Publicado en: | Journal of the American Academy of Dermatology Vol. 78; no. 2; pp. 270 - 271 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Feb2018
|
| 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=127284691&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127284691 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: Feb2018 vid: 78 iid: 2 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 127284691 127284691 NLM28969863 127284691 10.1016/j.jaad.2017.08.016 NLM28969863 127284691 ppf: 270 ppct: 1 formats: tig: atl: Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. aug: au: Marchetti, Michael A. Codella, Noel C.F. Dusza, Stephen W. Gutman, David A. Helba, Brian Kalloo, Aadi Mishra, Nabin Carrera, Cristina Celebi, M. Emre DeFazio, Jennifer L. Jaimes, Natalia Marghoob, Ashfaq A. Quigley, Elizabeth Scope, Alon Yélamos, Oriol Halpern, Allan C. affil: Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York sug: subj: Skin Neoplasms Microscopy Melanoma Diagnosis Algorithms Nevus Lentigo Skin Neoplasms Pathology Human Diagnosis, Computer Assisted Congresses and Conferences Melanoma Pathology Cross Sectional Studies ROC Curve Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source ab: Background: Computer vision may aid in melanoma detection.Objective: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.Methods: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant.Results: The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001).Limitations: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice.Conclusion: Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|