It is known that hair growth disorders and hair loss can cause personal distress and affect well‐being. Whilst clinical conditions remain a target for medical research, current research on hair follicle biology and hair growth control mechanisms also provides opportunities for a range of non‐medical and cosmetic interventions that have a modulating effect on the scalp and follicle function. Furthermore, an improvement of the hair fibre characteristics (cuticle structure, cortex size and integrity) could add to the overall positive visual effect of the hair array. Since phytochemicals are a popular choice because of their traditional appeal, this review provides a critical evaluation of the available evidence of their activity for hair benefit, excluding data obtained from animal tests, and offers recommendations on improving study validity and the robustness of data collection in pre‐clinical and clinical studies.
This review critically appraises the reported differences in human hair fibre within three related domains of research: hair classification approaches, fibre characteristics and properties. The most common hair classification approach is based on geo-racial origin, defining three main groups: African, Asian and Caucasian hair. This classification does not account sufficiently for the worldwide hair diversity and intergroups variability in curl, shape, size and colour. A global classification into eight curl types has been proposed but may be too complex for reproducibility. Beyond that, hair cross-sectional shape and area have been found to have an inverse relation to curl: straighter fibres are circular with larger cross-sectional area, whilst the curlier fibres are elliptical with smaller cross-sectional area. These geometrical differences have been associated with bilateral vs homogenous distribution of cortical cell in curly vs straight hair respectively. However, there is no sufficient data demonstrating significant differences in hair amino composition, but proteomic studies are reporting associations of some proteins with curly hair. Eumelanin's relative abundance has been reported in all hair colours except for red hair which has a high pheomelanin content. Higher tensile and fatigue strength of straight hair are reported, however, curly hair fragility is attributed to knotting, and crack and flow formations rather than the structural variations.African hair has been found to have the highest level of lipids, whilst the water sorption of Caucasian hair is the highest, and that of Asian hair the lowest. Not all comparative studies clearly report their hair sampling approaches. Therefore, to strengthen the robustness of comparative studies and to facilitate cross-study data comparisons, it is recommended that the following hair defining characteristics are reported in studies: hair cross sectional diameter/area, curl type, hair assembly colour, as well as where possible donor data (age/gender) and sample pooling approach.
AbstraitCette revue évalue de façon critique les différences rapportées sur les fibres capillaires humaines dans trois domaines de recherche connexes : les approches
During combing, individual fibres may not experience any significant loads and are unlikely to experience repetitive loads >10 g. The low number of comb strokes and low event probability is in keeping with consumers growing their hair long and in good condition. The data indicates the need for a significant rethink of methods used for product evaluation and claim substantiation.
Objective
The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.
Methods
Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms.
Results
The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.
Conclusions
This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.
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