Evaluating medical aesthetics treatments through evolved age-estimation models

Estimating a person's age from a facial image is a challenging problem with clinical applications. Several medical aesthetics treatments have been developed that alter the skin texture and other facial features, with the goal of potentially improving patient's appearance and perceived age. In this paper, this effect was evaluated using evolutionary neural networks with uncertainty estimation. First, a realistic dataset was obtained from clinical studies that makes it possible to estimate age more reliably than e.g. datasets of celebrity images. Second, a neuroevolution approach was developed that customizes the architecture, learning, and data augmentation hyperparameters and the loss function to this task. Using state-of-the-art computer vision architectures as a starting point, evolution improved their original accuracy significantly, eventually outperforming the best human optimizations in this task. Third, the reliability of the age predictions was estimated using RIO, a Gaussian-Process-based uncertainty model. Evaluation on a real-world Botox treatment dataset shows that the treatment has a quantifiable result: The patients' estimated age is reduced significantly compared to placebo treatments. The study thus shows how AI can be harnessed in a new role: To provide an objective quantitative measure of a subjective perception, in this case the proposed effectiveness of medical aesthetics treatments.