Establishment of a PCA model for skin health evaluation

Modeling of global skin aging indices among Caucasian and Asian women

BACKGROUND

The skin aging process is defined as the gradual degradation of several skin properties such as firmness, color, or the appearance of wrinkles. These properties can be assessed by trained experts, who perform an overall evaluation of the entire face.

OBJECTIVE

The objective of this paper is the construction of two Global Skin Aging Indices specifically designed to model the overall skin aging process of Caucasian and Asian women.

METHODS

Two hundred forty Asian women and 129 Caucasian women aged between 20 and 60 years old are recruited. Parameters related to wrinkles, sagging, elasticity, and skin tone are measured (clinically or instrumentally). The global skin aging index is defined as the normalized projection on the first principal component of a principal component analysis of the skin measurements. Then, linear regressions are performed between the indices and age of both panels.

RESULTS

The first principal component carries around 50% of the initial variance for both indices. Both Global Skin Aging Indices statistically correlate with age (R2 ≥ 0.7, p-value < 0.05). An equation linking the indices with age is computed.

CONCLUSION

The proposed indices are good indicators of the overall aging process for Caucasian and Asian women. They offer new approaches to assess antiaging product efficacy.

Artificial Intelligence in Dermatology: A Practical Introduction to a Paradigm Shift

Artificial Intelligence (AI) has surpassed dermatologists in skin cancer detection, but dermatology still lags behind radiology in its broader adoption. Building and using AI applications are becoming increasingly accessible. However, complex use cases may still require specialized expertise for design and deployment. AI has many applications in dermatology ranging from fundamental research, diagnostics, therapeutics, and cosmetic dermatology. The lack of standardization of images and privacy concerns are the foremost challenges stifling AI adoption. Dermatologists have a significant role to play in standardized data collection, curating data for machine learning, clinically validating AI solutions, and ultimately adopting this paradigm shift that is changing the way we practice.