Clinical validation of an automated system for supporting the early diagnosis of melanoma

Abstract

Background: Early diagnosis and surgical excision is the most effective treatment of melanoma. Well-trained dermatologists reach a high level of diagnostic accuracy with good sensitivity and specificity. Their performances increase using some technical aids as digital epiluminescence microscopy. Several studies describe the development of computerized systems whose aim is supporting dermatologists in the early diagnosis of melanoma. In many cases, the performances of those systems were comparable to those of dermatologists. However, this cannot tell us whether a system is able to support dermatologists. Actually, the computerized system might correctly recognize the same lesions that the dermatologist does, without providing them any useful advice and therefore being useless in recognizing early malignant lesions.

Purpose: We present a novel approach to enhance dermatologists' performances in the diagnosis of early melanoma. We provide results of our evaluation of a computerized system combined with dermatologists.

Methods: A Multiple-Classifier system was developed on a set of 152 cases and combined to a group of eight dermatologists to support them by improving their sensitivity.

Results: The eight dermatologists have average sensitivity and specificity values of 0.83 and 0.66, respectively. The Multiple-Classifier system performs as well as the eight dermatologists (sensitivity range: 0.75-0.86; specificity range: 0.64-0.89). The combination with the dermatologists shows an average improvement of 11% (P=0.022) of dermatologists' sensitivity.

Conclusion: Our results suggest that an automated system can be effective in supporting dermatologists because it recognizes different malignant melanomas with respect to the dermatologists.