SCAWA scales: A new digital tool for wrinkles clinical grading based on AI.

Objective: Clinical assessment of wrinkle depth is essential for efficacy evaluations of anti-ageing products. Standardized photographic scales, representative of different wrinkle depths are often used by experts to assign subjects reliable grades. These tools, based on real pictures, usually exist as hard copies (printed books or sheets) for in vivo gradings. Our project aims at developing a methodology to create digital standardized computer-generated scales, allowing photograph and real-life gradings, and providing raters with greater comfort, accessibility, and flexibility in their construction, thanks to the artificial intelligence significative contribution.

Methods: A completely new approach, based on machine learning, allows the creation of Standardized ColorFace® AI-based Wrinkle Assessment (SCAWA) scales. Instead of using real photographs, the scale images are computer-generated. A generative adversarial network (GAN) is trained to create realistic wrinkle samples that are finely controllable by exploring the GAN latent space. Finally, the scale images are selected among hundreds of artificial images depicting natural wrinkle appearances, such as the illustrated wrinkle evolution is well-detailed (small gaps between grades), morphologically stable, and mathematically linear according to a criterion of wrinkle conspicuous depth.

Results: The created 12-point scale for crow's feet wrinkle evaluation on ColorFace® pictures is proven to be realistic, linear, and robustly and accurately usable for photograph assessments. The scale coherence in terms of image ranking has been validated, as well as its reliability and acceptability in real conditions of use. Additionally, the wrinkle grades obtained by the SCAWA scale are well correlated (R = 0.94) with the ones obtained by the Skin Aging Atlas on the same pictures. The AI methodology and digital format brought also interesting side results, such as an enhanced harmonization between experts and a higher representativeness, that is, a decrease of out-of-range pictures.

Conclusion: SCAWA scale makes the most of machine learning to provide an innovative digital tool to ease wrinkles in visual assessment of pictures, while optimizing linearity, homogeneity, and accuracy aspects. The experts' enthusiastic feedback about the scale format and quality is promising regarding the adaptation of the methodology to other signs and a larger distribution of this tool in the market of cosmetic product efficacy assessment.