ArticlePediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology2022
Endotyping allergic rhinitis in children: A machine learning approach.
Article in Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Alterations in the gut virome of children with allergic rhinitis: enrichment of pro-inflammatory bacteriophages and depletion of fungal viruses.Microbiology spectrum · 2026Article
- Development of Artificial Intelligence for Quantitative Assessment of Nasal Inflammatory Cytology in Chronic Rhinitis by Whole-Slide Images.International forum of allergy & rhinology · 2026Article
- Current Insights Into Individualized Diagnosis and Treatment of Allergic Rhinitis Based on Phenotype and Endotype.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Advances in clinical examination techniques for allergic rhinitis: a review from traditional testing to precision diagnosis.Frontiers in allergy · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- The nasal-oral microbiome axis in allergic rhinitis: environmental triggers, microbial dysbiosis, and immune dysregulation.Frontiers in allergy · 2026Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- [Pediatric oral maxillofacial management and artificial intelligence].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2023Review
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Endotyping allergic rhinitis in children: A machine learning approach.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2022Article
- Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.Frontiers in endocrinology · 2022Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionThe diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data-driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children.
methodsA total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history were obtained from their parents. All children performed NC and skin prick tests. LCA was applied for identifying AR endotypes based on NC, using the R package poLCA. All the statistical analyses were performed using R 4.0.5 software. Statistical significance was set at p ≤ .05.
resultsLCA identified two classes: Class 1 (n = 126, 75%): higher frequency of children with moderate/large number of neutrophils (31.45%); almost all the children in this class had no mast cells (91.27%) and Class 2 (n = 42, 25%): higher frequency of children with moderate/large number of eosinophils (45.24%) and moderate/large number of mast cells (50%).
conclusionsThe present study used a machine learning approach for endotyping childhood AR, which may contribute to improve the diagnostic accuracy and to deliver personalized health care in the context of precision medicine.
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