Beauty technology company Haut.AI has published a study that shows AI can be used to enhance skin fluorescence photography to “allow early, non-invasive detection of skin conditions.”
The research explored state-of-the-art developments in skin fluorescence photography and combined it with AI algorithms for non-invasive skin diagnostics.
Fluorescence photography captures the natural glow emitted by molecules like collagen and porphyrins when skin is exposed to ultraviolet (UV) light.
It is anticipated that this approach could uncover skin issues such as acne, photoageing, and hyperpigmentation before they become visibly noticeable.
Diagnosing skin conditions before they start
The researchers said this technique could help skin care experts diagnose underlying issues earlier, track treatment progress more effectively, and gain deeper insights into skin ageing.
“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,” explained CEO and co-founder of Haut.AI, Anastasia Georgievskaya.
“We’re looking at a future where skin analysis is multimodal and utilises different aging models and biomarkers, such as using fluorescence spectroscopy, to make skin analysis more precise and predictive,” she continued.
Using AI in skin care diagnostics
The research explored the potential of combining AI with fluorescence photography to enhance skin diagnostics.
AI algorithms can analyse the data captured by fluorescence images, identifying minor 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.
It allows skin care professionals to make data-driven decisions for more personalised care and better treatment outcomes.
When it comes to combining AI with fluorescence photography, Haut.AI said its research “underscored the importance of using diverse datasets to ensure accuracy across different skin tones and types.”
The level of fluorescence emitted by the skin’s molecules can vary significantly depending on melanin levels and other characteristics, so AI needs to be trained on diverse datasets to help reduce biases that could lead to unequal diagnostic outcomes, making sure that AI-driven skincare solutions are inclusive for all.
