Response predictor for pigment reduction after one session of photo-based therapy using convolutional neural network: A proof of concept study.

Background: Identifying treatment responders after a single session of photo-based procedure for hyperpigmentary disorders may be difficult.

Objectives: We aim to train a convolutional neural network (CNN) to test the hypothesis that there exist discernible features in pretreatment photographs for identifying favorable responses after photo-based treatments for facial hyperpigmentation and develop a clinically applicable algorithm to predict treatment outcome.

Methods: Two hundred and sixty-four sets of pretreatment photographs of subjects receiving photo-based treatment for esthetic enhancement were obtained using the VISIA® skin analysis system.