Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study.
Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.
Department of Dermatology and Department of Heart Surgery and Biomedical Technology, University of Siena; LegaTumori, Siena, Italy. rubegni@unisi.it
Pietro
P
Rubegni
Marco
M
Burroni
Gabriele
G
Cevenini
Roberto
R
Perotti
Giordana
G
Dell'Eva
Paolo
P
Barbini
Michele
M
Fimiani
Lucio
L
Andreassi
D006801:Humans; D008163:Luminescent Measurements; D008545:Melanoma / Q000473:pathology*; D008853:Microscopy; D016571:Neural Networks, Computer; D009508:Nevus, Pigmented / Q000473:pathology*; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted; D012867:Skin / Q000473:pathology*; D012878:Skin Neoplasms / Q000473:pathology*