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.
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.
Authors: Sila Kurugol; Kivanc Kose; Jennifer G Dy; Dana H Brooks; Milind Rajadhyaksha; Brian Park Journal: J Invest Dermatol Date: 2014-09-03 Impact factor: 8.551
Authors: Milind Rajadhyaksha; Ashfaq Marghoob; Anthony Rossi; Allan C Halpern; Kishwer S Nehal Journal: Lasers Surg Med Date: 2016-10-27 Impact factor: 4.025