Enhancing skin aging parameter assessment in clinical trials: AI-Driven analysis of ultrasound images

Abstract

BackgroundHigh-frequency ultrasound (HFUS) imaging offers valuable insights into skin aging parameters in clinical trials. However, manual measurements are time-consuming and operator-dependent, necessitating the exploration of automated approaches. ObjectiveThis study aimed to develop, train, and validate an artificial intelligence model for automated HFUS image parameter acquisition in dermatological assessments. A secondary goal was its application in evaluating data from an anti-aging skin therapy clinical trial. MethodsSupervised machine learning algorithms were used to predict target variables from ultrasound images. The best models were selected through independent validation. A training dataset of 144 ultrasound skin images and a validation set of 40 images were employed. A clinical trial involved 34 female subjects with aging skin signs, randomized into two groups: (1) cosmetic formulation plus therapeutic unfocused ultrasound (TUS) and (2) placebo formulation plus TUS. HFUS imaging was conducted before and after a 4-week and 8-week study period. ResultsOur algorithms successfully predicted echogenicity and thickness variables, showing competitive performance. In the clinical trial, the treatment group exhibited a significant improvement in skin echogenicity, while no significant changes were observed in the placebo group. ConclusionThis study demonstrates the feasibility of automating the prediction of bioimage variables for anti-aging strategy development. Mathematical algorithms reduced HFUS image evaluations to minutes, expediting clinical trial progress.