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.

Fragmentation of the mitochondrial network in skin in vivo.

Mitochondria form dynamic networks which adapt to the environmental requirements of the cell. We investigated the aging process of these networks in human skin cells in vivo by multiphoton microscopy. A study on the age-dependency of the mitochondrial network in young and old volunteers revealed that keratinocytes in old skin establish a significantly more fragmented network with smaller and more compact mitochondrial clusters than keratinocytes in young skin. Furthermore, we investigated the mitochondrial network during differentiation processes of keratinocytes within the epidermis of volunteers. We observe a fragmentation similar to the age-dependent study in almost all parameters. These parallels raise questions about the dynamics of biophysical network structures during aging processes.