Background: Several studies have achieved high-level performance of melanoma detection using convolutional neural networks (CNNs). However, few have described the extent to which the implementation of CNNs 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 CNNs as an adjoining, decision-supporting system for physicians’ decisions.
|Number of pages||9|
|Journal||Journal of the European Academy of Dermatology and Venereology|
|Publication status||Published - 2020 Aug 1|
Bibliographical noteFunding Information:
Funding source This research was supported in part by the Global Frontier Program, through the Global Frontier Hybrid Interface Materials (GFHIM) of the National Research Foundation of Korea (NRF), and funded by the Ministry of Science, ICT & Future Planning (2013M3A6B1078872), in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1058971), and in part by the Yonsei University Wonju Campus Future-Leading Research Initiative in 2018 (RMS2 2018-62-0058). We would like to thank all the physicians who participated in the online survey, without whom this study would not have been possible.
© 2020 European Academy of Dermatology and Venereology
All Science Journal Classification (ASJC) codes
- Infectious Diseases