Matricardi PM, Monnati F, Palmieri L et al. J Allergy Clin Immunol. 2026 Mar 26:S0091-6749(26)00215-0. doi: 10.1016/j.jaci.2026.03.011.
ABSTRACT
Background
A precise etiological diagnosis of seasonal allergic rhinitis (SAR) is essential for a tailored prescription of its only curative treatment, allergen-specific immunotherapy (AIT). This is a challenging task in temperate climates, where most patients are polysensitized to multiple pollen with overlapping seasons.
Objective
The study aims to develop a modular, flexible and validated Clinical Decision Support System (CDSS) generated with Artificial Intelligence for the etiologic diagnosis of SAR.
Methods
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| Study Workflow |
Results
Three models best performing (AUROC >95%) have been then generated by ML training and tested on 2/3 and 1/3 patients, respectively.








