RESUMEN
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.
RESUMEN
BACKGROUND: Hidradenitis suppurativa (HS) is a painful chronic inflammatory skin disease that affects up to 4% of the European adult population. International Hidradenitis Suppurativa Severity Score System (IHS4) is a dynamic scoring tool that was developed to be incorporated into the doctor's daily clinical practice and clinical studies. This helps measure disease severity and guides the therapeutic strategy. However, IHS4 assessment is a time-consuming and manual process, with high inter-observer variability and high dependence on the observer's expertise. MATERIALS AND METHODS: We introduce the Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4), an automatic equivalent of IHS4 that deploys a deep learning model for lesion detection, called Legit.Health-IHS4net, based on the YOLOv5 architecture. AIHS4 was trained on Legit.Health-HS-IHS4, a collection of HS images manually annotated by six specialists and processed by a novel knowledge unification algorithm. RESULTS: Our results show that, with the current dataset size, our tool assesses the severity of HS cases with a performance comparable to that of the most expert physician. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials. CONCLUSION: Our work proves the potential usefulness of artificial intelligence in the practice of evidence-based dermatology: models trained on the consensus of large clinical boards have the potential to empower dermatologists in their daily practice and replace current standard clinical endpoints.
