In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours.

BACKGROUND

In vivo reflectance confocal microscopy (RCM) has been shown to be a valuable imaging tool in the diagnosis of melanocytic skin tumours. However, diagnostic image analysis performed by automated systems is to date quite rare.

OBJECTIVES

In this study, we investigated the applicability of an automated image analysis system using a machine learning algorithm on diagnostic discrimination of benign and malignant melanocytic skin tumours in RCM.

METHODS

Overall, 16,269 RCM tumour images were evaluated. Image analysis was based on features of the wavelet transform. A learning set of 6147 images was used to establish a classification tree algorithm and an independent test set of 10, 122 images was applied to validate the tree model (grouping method 1). Additionally, randomly generated 'new' learning and test sets, tumour images only and different skin layers were evaluated (grouping method 2, 3 and 4).

RESULTS

The classification tree analysis correctly classified 93.60% of the melanoma and 90.40% of the nevi images of the learning set. When the classification tree was applied to the independent test set 46.71 ± 19.97% (range 7.81-83.87%) of the tumour images in benign melanocytic skin lesions were classified as 'malignant', in contrast to 55.68 ± 14.58% (range 30.65-83.59%; t-test: P < 0.036) in malignant melanocytic skin lesions (grouping method 1). Further investigations could not improve the results significantly (grouping method 2, 3 and 4).

CONCLUSIONS

The automated RCM image analysis procedure holds promise for further investigations. However, to date our system cannot be applied to routine skin tumour screening.