Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population

Martin-Gonzalez, Manuel; Azcarraga, Carlos; Martin-Gil, Alba; Carpena-Torres, Carlos; Jaen, Pedro

doi:10.3390/ijerph19073892

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Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population

Manuel Martin-Gonzalez

^1,2,*,

Carlos Azcarraga

¹,

Alba Martin-Gil

Carlos Carpena-Torres

and

Pedro Jaen

^1,2

Service of Dermatology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain

Instituto Ramón y Cajal de Investigación Sanitaria, 28034 Madrid, Spain

Ocupharm Research Group, Department of Optometry and Vision, Faculty of Optics and Optometry, Complutense University of Madrid, 28037 Madrid, Spain

Author to whom correspondence should be addressed.

Academic Editors: Daniela Hartmann and Cristel Ruini

Int. J. Environ. Res. Public Health 2022, 19(7), 3892; https://doi.org/10.3390/ijerph19073892

Received: 25 February 2022 / Revised: 19 March 2022 / Accepted: 21 March 2022 / Published: 24 March 2022

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases and Skin Cancer)

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Abstract

(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma in a hospital population. (2) Methods: A retrospective study was performed using 232 dermoscopic images from the clinical database of the Ramón y Cajal University Hospital (Madrid, Spain). The skin lesions images, previously diagnosed as nevus (n = 177) or melanoma (n = 55), were analyzed by the quantusSKIN system, which offers a probabilistic percentage (diagnostic threshold) for melanoma diagnosis. The optimum diagnostic threshold, sensitivity, specificity, and accuracy of the quantusSKIN system to diagnose melanoma were quantified. (3) Results: The mean diagnostic threshold was statistically lower (p < 0.001) in the nevus group (27.12 ± 35.44%) compared with the melanoma group (72.50 ± 34.03%). The area under the ROC curve was 0.813. For a diagnostic threshold of 67.33%, a sensitivity of 0.691, a specificity of 0.802, and an accuracy of 0.776 were obtained. (4) Conclusions: The quantusSKIN system is proposed as a useful screening tool for melanoma detection to be incorporated in primary health care systems. View Full-Text

Keywords: melanoma; skin cancer; oncology; artificial intelligence; deep learning

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MDPI and ACS Style

Martin-Gonzalez, M.; Azcarraga, C.; Martin-Gil, A.; Carpena-Torres, C.; Jaen, P. Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population. Int. J. Environ. Res. Public Health 2022, 19, 3892. https://doi.org/10.3390/ijerph19073892

AMA Style

Martin-Gonzalez M, Azcarraga C, Martin-Gil A, Carpena-Torres C, Jaen P. Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population. International Journal of Environmental Research and Public Health. 2022; 19(7):3892. https://doi.org/10.3390/ijerph19073892

Chicago/Turabian Style

Martin-Gonzalez, Manuel, Carlos Azcarraga, Alba Martin-Gil, Carlos Carpena-Torres, and Pedro Jaen. 2022. "Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population" International Journal of Environmental Research and Public Health 19, no. 7: 3892. https://doi.org/10.3390/ijerph19073892

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Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population

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