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Article

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

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Service of Dermatology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain
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Instituto Ramón y Cajal de Investigación Sanitaria, 28034 Madrid, Spain
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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
(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 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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