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Augmented decision‐making for acral lentiginous melanoma detection using deep convolutional neural networks
Author(s) -
Lee S.,
Chu Y.S.,
Yoo S.K.,
Choi S.,
Choe S.J.,
Koh S.B.,
Chung K.Y.,
Xing L.,
Oh B.,
Yang S.
Publication year - 2020
Publication title -
journal of the european academy of dermatology and venereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 107
eISSN - 1468-3083
pISSN - 0926-9959
DOI - 10.1111/jdv.16185
Subject(s) - medicine , stage (stratigraphy) , convolutional neural network , concordance , confidence interval , acral lentiginous melanoma , dermatology , artificial intelligence , melanoma , computer science , cancer research , paleontology , biology
Background Several studies have achieved high‐level performance of melanoma detection using convolutional neural networks ( CNN s). However, few have described the extent to which the implementation of CNN s improves the diagnostic performance of the physicians. Objective This study is aimed at developing a CNN for detecting acral lentiginous melanoma ( ALM ) and investigating whether its implementation can improve the initial decision for ALM detection made by the physicians. Methods A CNN was trained using 1072 dermoscopic images of acral benign nevi, ALM and intermediate tumours. To investigate whether the implementation of CNN can improve the initial decision for ALM detection, 60 physicians completed a three‐stage survey. In Stage I, they were asked for their decisions solely on the basis of dermoscopic images provided to them. In Stage II , they were also provided with clinical information. In Stage III , they were provided with the additional diagnosis and probability predicted by the CNN . Results The accuracy of ALM detection in the participants was 74.7% (95% confidence interval [ CI ], 72.6–76.8%) in Stage I and 79.0% (95% CI , 76.7–81.2%) in Stage II . In Stage III , it was 86.9% (95% CI , 85.3–88.4%), which exceeds the accuracy delivered in Stage I by 12.2%p (95% CI , 10.1–14.3%p) and Stage II by 7.9%p (95% CI , 6.0–9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss‐κ of 0.436 [95% CI , 0.437–0.573] in Stage I, 0.506 [95% CI , 0.621–0.749] in Stage II and 0.684 [95% CI , 0.621–0.749] in Stage III ). Conclusions Augmented decision‐making improved the performance of and concordance between the clinical decisions of a diverse group of experts. This study demonstrates the potential use of CNN s as an adjoining, decision‐supporting system for physicians’ decisions.