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Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions., (Copyright 2002 Wiley-Liss, Inc.)

Epiluminescence light microscopy (ELM) has proven useful in the diagnosis of pigmented skin lesions (PSLs). However, in some cases this technique does not sufficiently increase the diagnostic accuracy in distinguishing pigmented Spitz naevi (PSNs) from melanoma. With the aim of obviating these problems of qualitative interpretation, methods based on the mathematical analysis of PSLs, such as digital dermoscopy analysis (DDA), have recently been developed. In the present study we used a digital dermoscope (DBDermo-MIPS, Dell'Eva-Burroni) to analyse PSNs and melanomas with similar clinical and dermoscopic features for any correlation between variables and to determine its discriminating power with respect to histological diagnosis. The 100 lesions underwent histological examination by three experienced dermatopathologists and were identified as PSNs (43) or melanomas (57). Thirty-six parameters were identified as possible discriminating variables and were grouped in four categories: geometry, colour, texture, and islands of colour. Statistical analysis was used to identify the variables with the highest discriminating power. Stepwise discriminant analysis selected only four variables: entropy, minimum diameter, red lesion value and peripheral dark (the means of these variables were higher in melanomas than in PSNs). Thus the combined use of digital dermoscopy and stepwise logistic discriminant analysis made it possible to single out the best objective variables for distinguishing PSN and melanoma.

Epiluminescence light microscopy (ELM) has been confirmed to be a useful tool for the diagnosis of pigmented skin lesions. The application of digital systems to epiluminescence represents the latest attempt to improve the diagnosis of cutaneous melanoma. The aim of this study was to compare the diagnostic accuracy of one of these systems, the DB-Dermo MIPS, with the accuracy of well-trained dermatologists using the ELM technique in order to establish the real usefulness of this instrument and to verify how much it can help the clinician make a diagnosis in a clinical setting. During a campaign for the early diagnosis of cutaneous melanoma, 311 patients with non-melanocytic lesions, common naevi, dysplastic naevi and melanomas underwent clinical diagnosis using ELM, computerized evaluation with DB-Dermo MIPS and skin biopsy. Sensitivity, specificity, true and negative predictive value were evaluated for epiluminescence and digital epiluminescence. Our study revealed that the inspection of pigmented skin lesions by digital epiluminescence has a better diagnostic accuracy than that of a trained dermatologist using the epiluminescence technique only. In our experience, this computerized system can play an essential role in the detection of early melanomas.

Focuses on a study which evaluated the utility of digital microscopy for analyzing atypical benign and malignant pigmented skin lesions exploiting digital numerical filtering and automatic measurements. Methodology of the study; Discussion of results; Advantage of the combined use of digital dermoscopy and stepwise logistic discriminant analysis.