Computer-assisted melanoma diagnosis

Abstract

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. Melanoma Res 25:537-542

Recent advances in managing and understanding seborrheic keratosis

“…The system evaluated 48 parameters to be studied as possible discriminant variables, grouped into four categories (geometries, colors, textures, and islands of color) integrated with three personal metadata items (sex, age, and site of lesion). Stepwise multivariate logistic regression of the data selected variables with the highest possible discriminant power 9 . Although the system has been developed primarily for computer-aided melanoma diagnosis, SKs can also be identified.…”

Validation of an integrated dermoscopic scoring method in an European teledermoscopy web platform: the iDScore project for early detection of melanoma

“…computer vision, deep learning and hardware-based). [20][21][22][23][24][25][26][27] Although these tools are increasingly developed, paradoxically they are poorly applied in clinical practice, probably due to the difficulty of data interpretation and physician distrust. 26,27 The key point is that dermatologists are still superior to computers because unlike current CAD models, they simultaneously evaluate medical history and the clinical data of suspected MSLs.…”

“…It is debated whether the diagnostic performance of dermoscopists is likely to be surpassed by that of computer‐assisted diagnosis (CAD) systems (e.g. computer vision, deep learning and hardware‐based) . Although these tools are increasingly developed, paradoxically they are poorly applied in clinical practice, probably due to the difficulty of data interpretation and physician distrust .…”

A High-Accuracy Mathematical Morphology and Multilayer Perceptron-Based Approach for Melanoma Detection

“…Since these techniques are not direct methods but are only part of the stage to improve the visualization of the skin, the precision lies in the dermatology specialist who evaluates the image delivered by the device. Therefore, it is essential to develop automated equipment that guarantees a certain level of reliability and efficiency [5][6][7][8][9][10][11][12].…”