Non-invasive prediction of dupilumab response in facial atopic dermatitis using early post-treatment reflectance confocal microscopy features.

Abstract

Non-invasive prediction of the efficacy of dupilumab on facial lesions in patients with atopic dermatitis (AD) was unresolved. To explore reflectance confocal microscopy (RCM), a non-invasive method, for predicting dupilumab efficacy in treating facial AD lesions and to achieve effective prediction via deep learning. 52 AD patients received dupilumab treatment between May 2023 and June 2025 were enrolled. Patients were categorized into 'responder' and 'non-responder' groups based on whether achieved 75% improvement from baseline in Eczema Area and Severity Index at week 16. RCM was utilized to evaluate facial lesions. Deep learning was employed to construct a prediction model for identifying intergroup differences. Dermal papillary dilation (AUC = 0.83, P < 0.001) and tortuous papillary capillary dilation (AUC = 0.81, P < 0.001) at week 4 correlated significantly with therapeutic non-response. The ResNet101-based deep learning model, trained on patients' RCM images of week 4 post-treatment scans, had a final test AUC of 0.796, with heatmaps illustrating its decision-making basis. The cohort size is relatively small. Early post-treatment RCM features can effectively predict the efficacy of dupilumab in facial AD lesions combined with deep learning.