Prediction of clinical response to excimer laser treatment in vitiligo by using neural network models

Abstract

Background: A predictive model may help to select likely responders and to anticipate treatment duration in vitiligo.

Methods: We aimed to develop a predictive rule based on data from a randomized trial of excimer laser in vitiligo. Information on 325 treated patches was available. The degree of repigmentation was assessed by digital image analysis of UVB-reflected photographs. Since no strong relationship between any single predictive parameter and outcome was initially documented, we relied on artificial neural networks.

Results: Using a time-response optimal threshold model, data were divided into 2 groups of responders and nonresponders. A discriminant network was trained in order to detect responders versus nonresponders. A regression network was subsequently used to compute repigmentation time in responders. The neural network discriminator achieved 66.46 +/- 5.37% (95% CI) overall accuracy. The mean absolute error of the neural network regressor was 19.5843 +/- 2.0930 with a root mean square error of 23.7156 +/- 2.2225.

Conclusion: Our study offers insight into the difficulty of clinical prediction in vitiligo and presents a way to develop an instrument with which to predict the clinical time response in patients treated by excimer laser.