Scalp application of the antioxidant piroctone olamine reduces hair shedding in an 8-week randomized, double-blind, placebo-controlled clinical study.

OBJECTIVE: Increasing scalp hair fullness is a global unmet consumer need. An approach to decrease hair shedding by reducing scalp stratum corneum oxidation via a combination of antioxidant and barrier-enhancing technologies has been previously demonstrated. The purpose of this study was to test the effectiveness of the individual antioxidant piroctone olamine in two different product forms (shampoo or leave-on product) for activity to improve hair retention. METHODS: Female subjects with self-perceived hair thinning participated in an 8-week, double-blind, placebo-controlled, randomized clinical study to evaluate either a piroctone olamine (PO) containing shampoo or a PO containing leave on treatment, each relative to their corresponding placebo formulation Too many periods. Results for phototrichograms, TEWL, and biomarker analysis of scalp condition for the shampoo treatments are discussed. Phototrichogram results are shared for the assessment of the leave on treatment. RESULTS: Statistically significant increases in hair amount were observed by phototrichogram after use of both PO-containing products versus placebo formulations. The PO shampoo treatment also significantly decreased oxidative stress on the hair and scalp, and improved scalp condition as assessed by TEWL and scalp biomarker values. CONCLUSION: These results illustrate the effectiveness of a cosmetic antioxidant to improve scalp condition thereby improving hair retention. The observed improvements in scalp condition are consistent with previous reports with other antioxidant technologies and suggest that the hair retention effect was achieved by preventing oxidative damage to the scalp.

Scalp application of antioxidants improves scalp condition and reduces hair shedding in a 24-week randomized, double-blind, placebo-controlled clinical trial.

OBJECTIVE: Increasing hair fullness is a global unmet need for many men and women. An approach to the problem is to decrease hair fall or shedding by reducing scalp stratum corneum oxidation and barrier damage to increase hair retention. This study evaluated a combination of functional antioxidants and barrier-enhancing cosmetic ingredients to improve scalp condition thereby enabling stronger hair anchorage and longer retention. METHODS: Male and female subjects with normal scalp condition and self-perceived hair thinning participated in a 24-week, double-blind, placebo-controlled, randomized clinical study assessing either a regimen of treatment shampoo and leave-on treatment containing functional antioxidant and barrier-enhancing agents or an identical placebo chassis shampoo control. The functional ingredients were piroctone olamine, zinc pyrithione, zinc carbonate, niacinamide, panthenol and caffeine. At baseline and after 8, 16 and 24 weeks of product use, several measurements were taken: hair shedding, total hair count (by phototrichogram), hair samples, TEWL and evaluation of biomarkers of scalp and hair conditions. Subjects also completed self-assessment questionnaires. RESULTS: Statistically significant effects for functional ingredient-containing treatment regimen versus a placebo control shampoo formulation were observed for reduced hair shedding, increased total hair count, reduced TEWL and improvement in scalp biomarker values. Subjects also noticed these improvements assessed via self-assessment questionnaires. CONCLUSIONS: These results establish that the use of functional antioxidant and barrier-enhancing agents to further improve scalp condition can enable a reduction in hair shedding and thus an increase in perceived hair fullness. The underlying improvements in scalp condition suggest the hair benefits were achieved as a result of improved scalp skin barrier and scalp condition leading to a viable preventative approach for hair thinning.

Development and qualification of a machine learning algorithm for automated hair counting.

OBJECTIVE: Determining the amount of hair on the scalp has always been an important metric of patient satisfaction for hair growth and hair retention technologies. While simple in concept, this measurement is a difficult, resource intensive task for the dermatologist and the research scientist. Specifically, counting and measuring hair in phototrichogram images is very time consuming and labour intensive. Due to cost, often only a fraction of available images is manually analysed. There is a need for an automated method that can significantly increase speed and throughput while reducing the cost of counting and measuring hair in phototrichogram images. METHODS: Recent advances in machine learning and deep convolutional neural networks (deep learning) have led to a revolution in the analysis of image, video, speech, text and other sensor data. Image diagnostics have seen remarkable improvements with completely automated methods outperforming both human experts and human-engineered analysis methods. Deep learning methods can also provide speed and cost benefits. To enable use of a deep learning, we created a data set of 288 manually annotated phototrichogram images with marked location and length of each hair (the training dataset). We designed a custom neural network architecture and custom image processing algorithms to best utilize the available training data and to maximize performance for hair counting and length measurement. The performance of the algorithm was qualified by comparing hair count and length measurements to an independent ground truth method, the semi-manual Canfield's Hair Metrix method. RESULTS: Leveraging deep neural networks, we have developed capability to apply machine learning to reduce the time needed to acquire data from phototrichograms of patients' scalp from months to seconds. Our algorithm enables fast and fully automated hair counting and length measurement. The algorithm shows high agreement with human manually assisted analysis (ground truth). CONCLUSIONS: We have trained and deployed an algorithm utilizing this technology and have demonstrated the reproducibility, accuracy and speed of this algorithm that, once deployed, requires little to no recurring cost or manual intervention for its operation. The method allows fast analysis of large number of images, reducing study cost and significantly reducing study analysis time.