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In dermatology, attempts at synergy between man and machine have mainly been made to improve melanoma diagnosis. The aim of the present study was to test an 'integrated digital dermoscopy analysis' (i-DDA) system with a series of melanocytic lesions that were benign and malignant in nature, and to evaluate its discriminating power with respect to histological diagnosis. In a retrospective study we used an i-DDA system to evaluate a series of 856 excised, clinically atypical pigmented skin lesions (584 benign and 272 malignant). The system evaluated 48 parameters to be studied as possible discriminant variables, grouped into four categories (geometries, colours, textures and islands of colour) integrated with three personal metadata items (sex, age and site of lesion) and presence/absence of three dermoscopic patterns (regression structures, blue-white veil and polymorphic vascular structures). Stepwise multivariate logistic regression of i-DDA data selected nine variables with the highest possible discriminant power. At the end of the stepwise procedure the percentage of cases correctly classified by i-DDA was 89.2% (100% sensitivity and 40.8% specificity). The limitations of the study included those associated with a retrospective design and the 'a priori' exclusion of nonmelanocytic skin lesions. By incorporating numerical digital features with personal data and some dermoscopic patterns into the learning process, the proposed i-DDA improved the performance of assisted melanoma diagnosis, with the advantage that our results can be objectively repeated in any other clinical setting.

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.