Decoding Hair Loss: AI-Enabled Analysis of Hair Growth and Follicle Dynamics as a Precision Medicine Tool For Alopecia

Background: Alopecia is a condition affecting millions of people worldwide, impacting not only physical health but also psychological well-being. Understanding the characteristics of hair and hair follicles is essential for developing effective treatments. In this study, we aim to analyze images of various areas to identify key parameters related to hair structure and distribution. Objectives: The main objective of this study is to develop an algorithm for analyzing hair images, exploring different shades and contrasts between hair and skin. This approach intends to provide a detailed quantitative assessment of the structural characteristics of hair and the distribution of hair follicles. Method: The images used in this study were acquired using the mobile application Scarletred®Vision, a validated and approved Software as a Medical Device (SaMD). The analysis includes the recognition of standardized SkinPatch sizes, used to deliver auto-color calibrated, high-resolution clinical images. A binary mask is then created to isolate the hair, allowing for contour detection and the removal of dimensional artifacts. Various parameters are calculated, including the length of the individual hair, as well as the average value, follicle positions, hair number per follicle, and hair thickness. Results: Preliminary results show that the patch sizes used for analysis were crucial for estimating the various hair parameters. Through this analysis, it was possible to estimate several significant parameters, including the average hair length, the total number of hairs present per analyzed area, and the frequency of multiple hairs within a single hair follicle, providing insights into follicular density. Additionally, hair thickness was evaluated in relation to patch sizes, contributing to a more comprehensive understanding of hair quality. A probabilistic estimate of the position of empty follicles was also conducted, suggesting areas potentially prone to alopecia. These results offer important insights into hair health and the mechanisms underlying hair loss. Conclusion: This study provides an innovative approach for analyzing hair and hair follicles through advanced image processing techniques. The results obtained can contribute to a better understanding of alopecia and the development of more targeted therapeutic interventions. Further research is needed to validate the models and deepen the analysis of the collected data.