Automatic Urticaria Activity Score: Deep Learning-Based Automatic Hive Counting for Urticaria Severity Assessment.

Autor:	Mac Carthy T; Department of Clinical Endpoint Innovation, Legit. Health, Bilbao, Spain., Hernández Montilla I; Department of Medical Computer Vision and PROMs, Legit.Health, Bilbao, Spain., Aguilar A; Department of Clinical Endpoint Innovation, Legit. Health, Bilbao, Spain., García Castro R; Dermatology Unit, Fundación Jiménez Díaz Teaching University Hospital, Madrid, Spain., González Pérez AM; Dermatology Unit, Zamora Hospital Complex, Zamora, Spain., Vilas Sueiro A; Dermatology Unit, Ferrol Teaching University Hospital Complex, Ferrol, Spain., Vergara de la Campa L; Dermatology Unit, Toledo Teaching University Hospital, Toledo, Spain., Alfageme F; Dermatology Unit, Puerta de Hierro Hospital, Majadahonda, Madrid, Spain., Medela A; Department of Medical Computer Vision and PROMs, Legit.Health, Bilbao, Spain.
Jazyk:	angličtina
Zdroj:	JID innovations : skin science from molecules to population health [JID Innov] 2023 Jul 12; Vol. 4 (1), pp. 100218. Date of Electronic Publication: 2023 Jul 12 (Print Publication: 2024).
DOI:	10.1016/j.xjidi.2023.100218
Abstrakt:	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. (© 2023 The Authors.)
Databáze:	MEDLINE
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