Wrinkles are the most visually obvious features of aging and are the prime target of a vast number of products (both medical and cosmetic). It is important for clinicians to be able to grade wrinkles objectively. This would enable clinicians to discern genuine effects of any anti-wrinkling agents. However, even with photographs, it remains difficult to truly and comprehensively grade wrinkles. This is because humans tend to rely on gross scoring scales, such as the Glogau photoaging scale. It is tedious and impractical to be able to highlight each and every wrinkle, especially those that are shallow or small, and judge it objectively. The paper developed a wrinkle detection algorithm based on a technique called “reversible jump Markov chain Monte Carlo framework with delayed rejection”. This system is able to accurately and rapidly detect wrinkles. The utilisation of such artificial intelligence represents the tip of an iceberg, and a change in practice paradigms that will come in due time.
