Automatic Urticaria Activity Score (AUAS): A Novel Technology for Urticaria Severity Assessment Based on Automatic High-Precision Hive Counting
Authors​
Taig Mac Carthy, Ignacio Hernández, Andy Aguilar, RubĂ©n GarcĂa Castro, Ana MarĂa González PĂ©rez, Alejandro Vilas Sueiro, Laura Vergara de la Campa, Fernando Alfageme Roldán, Alfonso Medela.
Summary​
Chronic urticaria (CU) is a chronic skin disease that affects up to 1% of the general population worldwide, with Chronic spontaneous urticaria (CSU) accounting for more than two thirds of all CU cases.
The Urticaria Activity Score (UAS) is a dynamic severity assessment tool that can be incorporated into the doctor’s 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 inter-observer variability and high dependence on the observer.
To tackle this issue, we introduce AUAS, an automatic equivalent of UAS that deploys a deep learning lesion-detecting algorithm, called Legit.Health-UAS-HiveNet. Our results show that our algorithm assesses the severity of CU cases with a performance comparable to that of expert physicians.
Furthermore, the algorithm can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials. This proves the usefulness of artificial intelligence in the practice of evidence-based medicine: algorithms trained on the consensus of large clinical boards have the potential of empowering clinicians in their daily practice and replacing current standard clinical endpoints in clinical trials.
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Available at https://dx.doi.org/10.2139/ssrn.4082860.