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

(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 (<i<n</i< = 177) or melanoma (<i<n</i< = 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 (<i<p</i< < 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..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:19

Enthalten in:

International Journal of Environmental Research and Public Health - 19(2022), 3892, p 3892

Sprache:

Englisch

Beteiligte Personen:

Manuel Martin-Gonzalez [VerfasserIn]
Carlos Azcarraga [VerfasserIn]
Alba Martin-Gil [VerfasserIn]
Carlos Carpena-Torres [VerfasserIn]
Pedro Jaen [VerfasserIn]

Links:

doi.org [kostenfrei]
doaj.org [kostenfrei]
www.mdpi.com [kostenfrei]
Journal toc [kostenfrei]
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Themen:

Artificial intelligence
Deep learning
Medicine
Melanoma
Oncology
R
Skin cancer

doi:

10.3390/ijerph19073892

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

DOAJ050965115