Effectiveness of a machine learning-enabled skincare recommendation for mild-to-moderate acne vulgaris: an 8-week evaluator-blinded randomised controlled trial (Preprint)

Abstract

UNSTRUCTURED Personalised skincare recommendations may be beneficial for treating mild-to-moderate acne vulgaris (AV). This study aimed to evaluate the effectiveness of a novel machine learning approach in predicting the optimal treatment for mild-to-moderate AV based on self-assessment and objective measures. A randomised, evaluator-blinded, parallel-group study was conducted on 100 patients recruited from an online database and randomised in a 1:1 ratio (groups A and B) based on their consent form submission. Groups A and B received customised product recommendations using a Bayesian machine learning model and self-selected treatments, respectively. The patients submitted self-assessed disease scores and photographs after the 8-week treatment. The primary and secondary outcomes were photograph evaluation by two board-certified dermatologists using the Investigator Global Assessment (IGA) scores and quality of life (QoL) measured using the Dermatology Life Quality Index (DLQI), respectively. Overall, 99 patients were screened, and 68 patients (mean age: 27 years) were randomised into groups A and B. IGA scores significantly improved after treatment in group A but not in group B. DLQI significantly improved in group A from 7.75 at baseline to 3.5 after treatment but reduced in group B from 7.53 to 5.3. IGA scores and DLQI significantly correlated in group A, but not in group B. Adverse reactions were reported in group B, but none in group A. Using a machine learning model for personalised skincare recommendations significantly reduced symptoms and improved severity and overall QoL of patients with mild-to-moderate AV, supporting the potential of machine learning-based personalised treatment options in dermatology.