作者
Michael A Marchetti, Noel CF Codella, Stephen W Dusza, David A Gutman, Brian Helba, Aadi Kalloo, Nabin Mishra, Cristina Carrera, M Emre Celebi, Jennifer L DeFazio, Natalia Jaimes, Ashfaq A Marghoob, Elizabeth Quigley, Alon Scope, Oriol Yélamos, Allan C Halpern
发表日期
2018/2/1
期刊
Journal of the American Academy of Dermatology
卷号
78
期号
2
页码范围
270-277. e1
出版商
Elsevier
简介
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 …
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MA Marchetti, NCF Codella, SW Dusza, DA Gutman… - Journal of the American Academy of Dermatology, 2018
