Skin Ageing

Skin color, how easily a person burns or tans in the sun (skin phototype), and the amount of chronic sun damage in the skin are important factors in skin health. These characteristics influence a person's risk of skin cancer, how skin diseases appear, how well treatments work, and how accurately doctors and artificial intelligence (AI) systems can diagnose skin conditions. However, current methods for classifying these characteristics are often imprecise and rely heavily on subjective assessments. As a result, both healthcare professionals and patients may incorrectly classify skin type, which can lead to inaccurate risk assessments and less personalized care. This study aims to develop and validate AI algorithms that can accurately classify skin pigmentation, skin phototype, and accumulated sun damage using photographs of the skin. Unlike existing approaches, the study combines several different methods to create a more objective "ground truth" for training the AI. These methods include skin color measurements using spectrophotometry or colorimetry, assessments using the Monk Skin Tone Scale, questionnaires about sun sensitivity, and clinical evaluations by trained observers. By combining these data sources, the researchers hope to create a more reliable and scientifically robust classification system. The study will recruit adults aged 18 years and older from several countries, including countries from all continents. Participants will complete a questionnaire about their skin, propensity to burn and sun exposure history. Researchers will then take standardized close-up and dermoscopic images of the skin on the arm and forearm, measure skin pigmentation using objective instruments when available, and assess skin phototype and sun damage. No invasive procedures will be performed, and no personally identifiable information will be collected. The collected images and measurements will be used to train deep learning AI models. The researchers aim to develop algorithms that can classify skin pigmentation with at least 85% accuracy, skin phototype with at least 75% accuracy, and sun damage with at least 80% accuracy compared with the combined reference assessments. The algorithms will then be tested in independent datasets, including large dermatology image databases from Sweden, to evaluate how well they perform in different populations. The study has several potential benefits. More accurate classification of skin characteristics could improve personalized skin cancer risk assessments and allow prevention advice to be tailored to individual needs. This may help identify people who would benefit from closer surveillance and stronger sun protection recommendations while avoiding unnecessary restrictions for people at lower risk. Improved classification could also enhance the diagnosis and management of inflammatory skin diseases and skin cancers, which can appear differently in people with different skin tones. An additional goal is to address known biases in dermatology AI systems, which often perform less accurately in individuals with darker skin. By including participants with a wide range of skin tones and backgrounds, the researchers aim to contribute to the benchmarking of AI-driven medical devices wich hopefully can result in the development of fairer and more equitable AI tools. The study involves minimal risk. Only photographs of the arm and forearm will be taken, and researchers will avoid capturing tattoos, prominent scars, or other identifying features. All data will be stored securely and only accessible to authorized researchers. The potential benefits of improving skin disease diagnosis, skin cancer prevention, and fairness in medical AI are considered to outweigh the small privacy risks associated with participation.

This clinical study is conducted at one study site and in 165 women with visible signs of skin aging. It compares after 13 weeks the benefit of a combined oral collagen supplement and topical collagen serum regimen compared with oral collagen alone and topical collagen serum alone in reducing in women presenting visible signs of skin ageing .

The purpose of this study is to build insights to understand how the placebo and active impacts the skin microbiome in women with high skin sensitivity (SS10 \> 13) and sun exposure. Microbiome samples will be taken at baseline after 2 weeks of using the placebo (base without active) as a run-in period followed by the usage of placebo for an additional 4 weeks. The participants will then start the usage of active formula for an additional 8 weeks with microbiome samples collected at the 10-week and 14-week. This will allow us to observe how the microbiome changes over time after the product usage of both placebo and active formulations relative to the pre-regimen condition. In addition, headshot photos will be taken using the Haut AI application at each time point concurrent to the microbiome sample collection to examine changes in skin appearances after the regimen.

This clinical study investigates the effects and safety of a topical cosmetic product containing postbiotics (Bifida ferment extract and Pediococcus ferment extract) in women aged 35-60 with visible signs of skin aging. The study is a 3-month, single-center, intra-subject controlled trial involving 45 Caucasian women with sensitive or normal skin. Participants will apply 1 ml of the product twice daily. Clinical evaluations will be conducted at baseline and at 1 month, 2 months, and 3 months, using validated dermatological scales and non-invasive instruments (AEVA3D, Mexameter®, MoistureMap®, Cutometer®, Tewameter®, Clarius®, Glossymeter®). Safety will be assessed through systematic monitoring of adverse events, with serious adverse events expected to remain below 1%. Subjective perception will be evaluated via structured questionnaires.