The study highlights the power of AI to enhance skin fluorescence photography, allowing early, non-invasive detection of skin conditions. This approach allows skincare experts to diagnose underlying issues earlier, track treatment progress more effectively, and offer deeper insights into skin ageing.
Fluorescence photography is an advanced technique that captures the natural glow emitted by molecules like collagen and porphyrins when the skin is exposed to ultraviolet (UV) light. The study highlights how this approach can uncover skin issues such as acne, photoageing, and hyperpigmentation—frequently before they become visibly noticeable.
Building on these findings, the paper explores the potential of combining AI with fluorescence photography to enhance skin diagnostics. AI algorithms can analyse the vast data captured by fluorescence images, identifying patterns and subtle changes that may be impossible to detect manually. This means AI can pinpoint early indicators of skin conditions with greater accuracy. Additionally, AI has the ability to monitor skin changes over time, providing insights into how skin conditions evolve and how treatments are working. This allows skincare professionals to make decisions for better treatment outcomes.
“Fluorescence photography allows us to see what the human eye often cannot, and when combined with AI, we’re unlocking entirely new levels of skin diagnostics,” said Anastasia Georgievskaya, CEO and co-founder of Haut.AI. “We’re looking at a future where skin analysis is multimodal and utilises different ageing models and biomarkers, such as using fluorescence spectroscopy, to make skin analysis more precise and predictive.”
When it comes to combining AI with fluorescence photography, Haut.AI’s research underscores the importance of using diverse datasets to ensure accuracy across different skin tones and types. Since the level of fluorescence emitted by the skin’s molecules can vary significantly depending on melanin levels and other characteristics, training AI on diverse datasets is essential. This helps reduce biases that could lead to unequal diagnostic outcomes, making sure that AI-driven skincare solutions are fair, effective, and inclusive for all, regardless of their unique skin profile.
Article:
Chekanov, K., Danko, D., Tlyachev, T., Kiselev, K., Hagens, R., & Georgievskaya, A. (2024). State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis. Life 2024, 14(10), 1271. https://doi.org/10.3390/life14101271
