Use of Electrical Impedance Spectroscopy (EIS) for Early Diagnosis of Skin Damage

Summary

Participation Requirements

Description

This is a clinical study designed to evaluate if the DermaSense device can be a system of an automated analysis and classification of skin lesions aiming to provide a comprehensive, reliable and cost-effective approach to assist the Dermatologists' decision (biopsy, dermoscopy, histopathological ana...

This is a clinical study designed to evaluate if the DermaSense device can be a system of an automated analysis and classification of skin lesions aiming to provide a comprehensive, reliable and cost-effective approach to assist the Dermatologists' decision (biopsy, dermoscopy, histopathological analysis etc). The study enrollment will continue until a minimum of 180 subjects are enrolled in the study. Second Department of Dermatology, School of Medicine, Faculty of Health Sciences of the Aristotle University of Thessaloniki, at the "Papageorgiou" General Hospital will participate in this study. Dermatologists participating in the study will perform the patient and lesion assessment for biopsy as standard of care at the clinic. EIS (electrical impedance spectroscopy) measurements will be done once per participant recommended by the Dermatologist based on their clinical interest in various areas of the skin. Two sets of measurements will be performed for each participant, either at a nevus in contrast with a nearby clear patch of skin, or at a skin damage area (melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), etc) in contrast with a nearby clear patch of skin. Also, more repeated measurements may be needed to determine the validity of the measurement result. Evaluation and analysis of the measurement data will be done by the DermaSense team and will be used not only for applying appropriate statistical correlations compared to established dermatological diagnostic methods (eg derm?scopy, histopathological examinations, biopsies etc.) but also for training in artificial intelligence and machine learning methods. Finally, the participants will sign a consent form that is consistent with bioethical rules and according with EU GDPR regulation to protect their personal data.

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