Abstract
Chronic urticaria is a chronic skin disease that affects up to 1% of the general population worldwide, with chronic spontaneous urticaria accounting for more than two-thirds of all chronic urticaria cases. The Urticaria Activity Score (UAS) is a dynamic severity assessment tool that can be incorporated into daily clinical practice, as well as clinical trials for treatments. The UAS helps in measuring disease severity and guiding the therapeutic strategy. However, UAS assessment is a time-consuming and manual process, with high interobserver variability and high dependence on the observer. To tackle this issue, we introduce Automatic UAS, an automatic equivalent of UAS that deploys a deep learning, lesion-detecting model called Legit.Health-UAS-HiveNet. Our results show that our model assesses the severity of chronic urticaria cases with a performance comparable to that of expert physicians. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new end point in clinical trials. This proves the usefulness of artificial intelligence in the practice of evidence-based medicine; models trained on the consensus of large clinical boards have the potential of empowering clinicians in their daily practice and replacing current standard clinical end points in clinical trials.