Quantitative characterization of eosinophilia in nasal polyps with AI-based single cell classification

Martin Stampe; Ida Skovgaard Christiansen; Vibeke Backer; Kasper Aanæs; Anne-Sophie Homøe; Jens Tidemandsen; Emilie Neumann Nielsen; Sigrid Louise Hjorth Rasmussen; Rasmus Hartvig; Katalin Kiss; Thomas Hartvig Lindkær Jensen

doi:10.1002/alr.23454

Quantitative characterization of eosinophilia in nasal polyps with AI-based single cell classification

Int Forum Allergy Rhinol. 2025 Feb;15(2):188-190. doi: 10.1002/alr.23454. Epub 2024 Sep 20.

Affiliations

¹ Department of Pathology, Rigshospitalet, Copenhagen, Denmark.
² Department of Head and Neck Surgery, Hovedortocentret, Rigshospitalet, Copenhagen, Denmark.
³ Department of Science and Environment, Roskilde University, Roskilde, Denmark.
⁴ SUND, University of Copenhagen, Copenhagen, Denmark.

Abstract

Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classification network. Single cell binary classification networks can reliably help quantify eosinophils in nasal polyps.

Keywords: chronic rhinosinustis; disease severity; eosinophilic rhinitis; nasal polyposis.

MeSH terms

Artificial Intelligence*
Eosinophilia* / diagnosis
Eosinophilia* / pathology
Eosinophils* / pathology
Humans
Nasal Polyps* / diagnosis
Nasal Polyps* / immunology
Nasal Polyps* / pathology
Single-Cell Analysis* / methods