Case 01. Image x Healthcare
Hair Diagnosis through Cuticle Analysis
In collaboration with Nakano Seiyaku Co., Ltd., a manufacturer and distributor of hair care cosmetics and quasi-drugs, we have established a technology that enables objective and quantitative evaluation of hair—previously dependent on the subjective judgment of stylists and researchers—by leveraging proprietary AI image recognition and machine learning.
Traditional Challenges
Visual inspection has long been the primary method of hair evaluation. Because it requires no special equipment and can be performed easily through visual comparison, it has been widely used across stylists, hair care companies, and researchers. However, since evaluators judge hair color, shine, thickness, density, and damage intuitively, the results tend to depend heavily on their subjectivity. Additionally, evaluating the number of overlapping cuticle layers requires cross-sectional images of hair, which need special equipment and expertise, making the evaluation difficult.
In recent years, AI-based hair evaluation has been proposed to improve objectivity. However, because existing AI models are trained on subjective data such as visual five-level grading, they have failed to fundamentally solve the problem.
How Our Technology Solves It
By applying our AI image analysis service GeXeL—built on proprietary segmentation technology—we succeeded in automatically identifying and segmenting the contours of hair-surface cuticles. We then analyzed hair-surface images from approximately 150 men and women aged 10 to 70, and examined correlations between the number of overlapping cuticle layers and feature values derived from cuticle-bounded regions (diagonal width, long-to-short side ratio of the bounding rectangle, area, etc.).
As a result, we discovered that certain feature values strongly correlate with the number of overlapping cuticle layers. By combining AI image recognition with machine learning techniques, we can now accurately estimate the number of overlapping cuticle layers without observing hair cross-sections.
Contribution of This Technology
The number of overlapping cuticle layers is a key indicator of hair condition, influencing hair texture and resistance to damage. This newly established technology enables delivery of appropriate hair care tailored to each person's hair condition, realizing more scientific and reliable hair care solutions.
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