Augmented Decision Making for Acral Lentiginous Melanoma Detection Using Deep Convolutional Neural Networks.

Publication date: Jan 09, 2020

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.

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.

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.

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%p-14.3%p) and Stage II by 7.9%p (95% CI, 6.0%p-9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss-? 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).

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.

Lee, S., Chu, Y.S., Yoo, S.K., Choi, S., Choe, S.J., Koh, S.B., Chung, K.Y., Xing, L., Oh, B., and Yang, S. Augmented Decision Making for Acral Lentiginous Melanoma Detection Using Deep Convolutional Neural Networks. 25431. 2020 J Eur Acad Dermatol Venereol.

Concepts Keywords
Benign Articles
CNN Artificial neural networks
Concordance Melanoma
Confidence Interval Computational neuroscience
Convolutional Neural Networks Cancer
Melanoma Emerging technologies
Nevi Academic disciplines
Probability Organ systems
Survey Acral lentiginous melanoma
Convolutional neural network
Deep learning
Lentigo
Neural network
Artificial intelligence

Semantics

Type Source Name
disease MESH Melanoma
pathway KEGG Melanoma
disease MESH nevi
disease MESH diagnosis

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