Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features

Abstract

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.

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

“…Daniels et al 26 . implemented machine learning on a dataset of images depicting virgin and blonde hair before and after being treated with a shampoo and conditioner combination designed to add volume, smooth the hair cuticle, and prevent flyaways.…”

“…Daniels et al. 26 implemented machine learning on a dataset of images depicting virgin and blonde hair before and after being treated with a shampoo and conditioner combination designed to add volume, smooth the hair cuticle, and prevent flyaways. The hair alignment, local, and global hair volumes, and hair volumes were automatically measured from the hair pictures.…”