0000000000408670
AUTHOR
John Paoli
Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety.
Summary Background Even though progress has been made, the detection of melanoma still poses a challenge. In light of this situation, the Nevisense electrical impedance spectroscopy (EIS) system (SciBase AB, Stockholm, Sweden) was designed and shown to have the potential to be used as an adjunct diagnostic tool for melanoma detection. Objectives To assess the effectiveness and safety of the Nevisense system in the distinction of benign lesions of the skin from melanoma with electrical impedance spectroscopy. Methods This multicentre, prospective, and blinded clinical study was conducted at five American and 17 European investigational sites. All eligible skin lesions in the study were exami…
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed
Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions
Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasi…
Electrical impedance spectroscopy as a potential adjunct diagnostic tool for cutaneous melanoma.
Background Previous studies have shown statistically significant differences in electrical impedance between various cutaneous lesions. Electrical impedance spectroscopy (EIS) may therefore be able to aid clinicians in differentiating between benign and malignant skin lesions. Objectives The aim of the study was to develop a classification algorithm to distinguish between melanoma and benign lesions of the skin with a sensitivity of at least 98% and a specificity approximately 20 per cent higher than the diagnostic accuracy of dermatologists. Patients/Methods A total of 1300 lesions were collected in a multicentre, prospective, non-randomized clinical trial from 19 centres around Europe. Al…