RT Journal Article
T1 Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population
A1 Martin Gonzalez, Manuel
A1 Azcárraga Llobet, Carlos
A1 Martín Gil, Alba
A1 Carpena Torres, Carlos
A1 Jaén Olasolo, Pedro
AB 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.
PB MDPI
SN 1660-4601
YR 2022
FD 2022-03-24
LK https://hdl.handle.net/20.500.14352/72391
UL https://hdl.handle.net/20.500.14352/72391
LA eng
DS Docta Complutense
RD 5 abr 2025