AI Nail Art Customization: Enhancing User Experience Through Skin Tone and Nail Disease Detection Using Self-Supervised Learning

The lack of advanced technology in nail care and disease detection limits the ability to achieve personalized nail aesthetics and maintain healthy nails, as many individuals are unable to detect and address underlying conditions. In this paper, the proponents developed a mobile app to bridge this gap. The app personalizes nail art by detecting user skin tones and analyzing nail health with self-supervised learning (SSL) techniques. This innovative approach provides tailored nail art recommendations that enhance the user's natural appearance and promotes nail health through a built-in diagnostic tool for detecting nail diseases and offering medical advice. Evaluated under ISO Standard 25010, the app received high ratings for functionality, performance efficiency, compatibility, reliability, and usability, with means of 3.34, 3.38, 3.63, 3.29, and 3.47, respectively. The developed AI mobile application integrated with self-supervised machine learning has 86.5% overall accuracy rate. Thus, these results underscore the app's effectiveness in delivering an intuitive, user-centered experience that prioritizes both aesthetics and nail health. Future improvements are recommended, such as expanding the algorithm for a broader range of skin tones, enhancing nail disease detection capabilities, simplifying the interface, and increasing device compatibility. This application not only aims to provide users with visually pleasing and personalized nail art but also promotes healthier nail care practices through advanced AI-driven features. With its dual focus on aesthetics and health, this mobile app represents a significant step forward in personalized, data-driven nail care solutions.