Artificial Intelligence (AI)-assisted readout method for the evaluation of skin prick automated test results

Sven F. Seys (), Valérie Hox, Adam M. Chaker, Glynnis Greve, Winde Lemmens, Anne-Lise Poirrier, Eline Beckers, Rembert Daems, Zuzana Diamant, Carmen Dierickx, Peter W. Hellings, Caroline Huart, Claudia Jerin, Mark Jorissen, Dirk Loeckx, Hanne Oscé, Karolien Roux, Mark Thompson, Sophie Tombu, Saartje Uyttebroek, Andrzej Zarowski, Senne Gorris and Laura Gerven
Additional contact information
Sven F. Seys: Hippo Dx
Valérie Hox: Cliniques Universitaires Saint-Luc
Adam M. Chaker: Technical University of Munich
Glynnis Greve: ZAS Sint-Augustinus
Winde Lemmens: ZOL
Anne-Lise Poirrier: CHU Liège
Eline Beckers: ZOL
Rembert Daems: Hippo Dx
Zuzana Diamant: KU Leuven
Carmen Dierickx: ZOL
Peter W. Hellings: KU Leuven
Caroline Huart: Cliniques Universitaires Saint-Luc
Claudia Jerin: Technical University of Munich
Mark Jorissen: UZ Leuven
Dirk Loeckx: Hippo Dx
Hanne Oscé: ZAS Sint-Augustinus
Karolien Roux: Dept Clin Pharm & Pharmacol
Mark Thompson: Zurich University of Applied Sciences
Sophie Tombu: CHU Liège
Saartje Uyttebroek: UZ Leuven
Andrzej Zarowski: ZAS Sint-Augustinus
Senne Gorris: Hippo Dx
Laura Gerven: KU Leuven

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: Abstract The skin prick test (SPT) is the gold standard for diagnosing allergic sensitization to aeroallergies. The Skin Prick Automated Test (SPAT) device has previously demonstrated reduced variability and more consistent test results compared to manual SPT. The current study aims to develop and validate an artificial intelligence (AI) assisted readout method to support physicians in interpreting skin reactions following SPAT. To train the AI algorithm, 7812 wheals (651 patients) are manually labeled. To validate the AI measurement, the longest wheal diameter of 2604 wheals (217 patients) is measured by the treating physician and compared to the AI measurement. In addition, AI-assisted readout is validated on a separate test cohort of 95 patients (1140 wheals). We demonstrate that the AI measurements of the longest wheal diameter exhibit a strong correlation with the physician’s measurements. The AI algorithm shows a specificity of 98·4% and sensitivity of 85·0% in determining positive or negative test results in the validation cohort. In the test cohort, physicians adjust 5·8% of AI measurements, leading to a change in the test interpretation for only 0·5% of cases. AI-assisted readout significantly reduces inter- and intra-observer variability and readout time compared to manual physician measurements. Altogether, the AI-assisted readout method demonstrates high accuracy, with minimal misclassification of test results. Adding AI to SPAT further improves standardization across the SPT process, significantly reducing observer variability and time to readout.

Date: 2025
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DOI: 10.1038/s41467-025-64334-w

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