Publications by authors named "Sven Clemann"

Background: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible.

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Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the "aging clocks" varying in biological relevance, ease of use, cost, actionability, interpretability, and applications.

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Background: It would be a benefit if time-saving, non-invasive methods could give hints for diagnosing systemic sclerosis. To investigate the skin of patients with systemic sclerosis using confocal laser scanning microscopy in vivo and to develop histometric parameters to describe characteristic cutaneous changes of systemic sclerosis observed by this new technique, we conducted an exploratory study.

Materials And Methods: Fifteen patients with systemic sclerosis treated with extracorporal photopheresis were compared with 15 healthy volunteers and 10 patients with other disorders also treated with extracorporal photopheresis.

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Background/aims: The confocal laser scanning microscope Vivascope (Lucid, Henrietta) allows skin to be studied in real-time with a resolution of 0.5 microm horizontal and 1.3 microm vertical in vivo.

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