Monique Thissen1,2,3,4, Andreea Udrea5, Michelle Hacking1, Tanja von Braunmuehl6, Thomas Ruzicka6. 1. 1 Department of Dermatology, Catharina Hospital , Eindhoven, The Netherlands . 2. 2 Department of Dermatology, Maastricht University Medical Centre+, Maastricht University , Maastricht, The Netherlands . 3. 3 GROW Oncology, Maastricht University , Maastricht, The Netherlands . 4. 4 School for Oncology and Developmental Biology, Maastricht University , Maastricht, The Netherlands . 5. 5 Department of Automatic Control and Systems Engineering, University Politehnica of Bucharest , Bucharest, Romania . 6. 6 Department of Dermatology and Allergology, Ludwig-Maximilian University , Munich, Germany .
Abstract
BACKGROUND: With the advent of smartphone devices, an increasing number of mHealth applications that target melanoma identification have been developed, but none addresses the general context of melanoma and nonmelanoma skin cancer identification. INTRODUCTION: In this study a smartphone application using fractal and classical image analysis for the risk assessment of skin lesions is systematically evaluated to determine its sensitivity and specificity in the diagnosis of melanoma and nonmelanoma skin cancer along with actinic keratosis and Bowen's disease. MATERIALS AND METHODS: In the Department of Dermatology, Catharina Hospital Eindhoven, The Netherlands, 341 melanocytic and nonmelanocytic lesions were imaged using SkinVision app; 239 underwent histopathological examination, while the rest of 102 lesions were clinically diagnosed as clearly benign and not removed. The algorithm has been calibrated using the images of the first 233 lesions. The calibrated version of the algorithm was used in a subset of 108 lesions, and the obtained results were compared with the medical findings. RESULTS: On the 108 cases used for evaluation the algorithm scored 80% sensitivity and 78% specificity in detecting (pre)malignant conditions. DISCUSSION: Although less accurate than the dermatologist's clinical eye, the app may offer support to other professionals who are less familiar with differentiating between benign and malignant lesions. CONCLUSION: An mHealth application for the risk assessment of skin lesions was evaluated. It adds value to diagnosis tools of its type by taking into consideration pigmented and nonpigmented lesions all together and detecting signs of malignancy with high sensitivity.
BACKGROUND: With the advent of smartphone devices, an increasing number of mHealth applications that target melanoma identification have been developed, but none addresses the general context of melanoma and nonmelanoma skin cancer identification. INTRODUCTION: In this study a smartphone application using fractal and classical image analysis for the risk assessment of skin lesions is systematically evaluated to determine its sensitivity and specificity in the diagnosis of melanoma and nonmelanoma skin cancer along with actinic keratosis and Bowen's disease. MATERIALS AND METHODS: In the Department of Dermatology, Catharina Hospital Eindhoven, The Netherlands, 341 melanocytic and nonmelanocytic lesions were imaged using SkinVision app; 239 underwent histopathological examination, while the rest of 102 lesions were clinically diagnosed as clearly benign and not removed. The algorithm has been calibrated using the images of the first 233 lesions. The calibrated version of the algorithm was used in a subset of 108 lesions, and the obtained results were compared with the medical findings. RESULTS: On the 108 cases used for evaluation the algorithm scored 80% sensitivity and 78% specificity in detecting (pre)malignant conditions. DISCUSSION: Although less accurate than the dermatologist's clinical eye, the app may offer support to other professionals who are less familiar with differentiating between benign and malignant lesions. CONCLUSION: An mHealth application for the risk assessment of skin lesions was evaluated. It adds value to diagnosis tools of its type by taking into consideration pigmented and nonpigmented lesions all together and detecting signs of malignancy with high sensitivity.
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