Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

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. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging.

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. Here we present and investigate a novel non-invasive class of visual photographic biomarkers of aging. We developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation.

Histometric data obtained by in vivo confocal laser scanning microscopy in patients with systemic sclerosis.

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. All subjects were investigated using confocal laser scanning microscopy in vivo. RESULTS: Micromorphologic characteristics of skin of patients with systemic sclerosis and measuring parameters for melanisation, epidermal hypotrophy, and fibrosis for dislocation of capillaries by collagen deposits in the papillary dermis were evaluated. An interesting finding was an increased thickness of the tissue in the dermal papillae superior to the first dermal papilla vessel. It was also possible to reproduce characteristic histologic features by confocal laser scanning microscopy in vivo. Histometric parameters for fibrosis and vascular features developed in this study showed significant differences in patients with systemic sclerosis compared to controls. CONCLUSIONS: Although the predominant histopathological features in systemic sclerosis are findings of the reticular dermis and the subcutis, and in histopathological investigation the epidermis seems to remain unaffected by the disease, we have demonstrate some characteristic differences in the epidermis and papillary dermis by confocal laser scanning microscopy in vivo. Some of them have not been described so far. However, to use this technique as a tool for diagnosis and/or staging of systemic sclerosis, further studies are needed investigating the sensitivity and specificity of the histometric parameters developed in this study.

Age related changes of human skin investigated with histometric measurements by confocal laser scanning microscopy in vivo.

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. In this study, we present the results of a comparison between the skin of an older and a younger group of volunteers by in vivo histometric measurements. METHODS: To investigate changes caused by age, 13 young (18-25years) and 13 older (>65years) volunteers were examined. The following parameters were measured using the Vivascope at the volar forearm: minimal thickness of the epidermis (E(min)), size of cells in the granular layer (A(gran)), thickness of the horny layer (DSC), thickness of the basal layer (DSB) and number of dermal papillae per area (Papl). The image analysis program image tool was used to measure the size of the cells and the thickness of the basal layer. RESULTS: The older group of volunteers showed a significant increase in E(min), no significant change in DSC, a significant decrease in dermal papillae and in the thickness of the basal layer, and an increase in A(gran) compared to the younger group. CONCLUSIONS: Histometric measurements by in vivo confocal laser scanning microscopy are a sensitive and non-invasive tool for characterizing and quantifying histological changes of the epidermis and papillary dermis due to ageing.