Section: 
rhinology issues
rhinology issuesAnalysis of association between the nasal microbiome and treatment outcomes of persistent allergic rhinitis using artificial intelligence algorithms
E. G. Portenko (1), K. B. Dobrynin (2), R. A. Trigubenko (3)
(1), (2), (3) Tver State Medical University, Tver, 170100, Russian Federation
UDK: УДК 616.211-002.2-085:616-074:004.8:579.23
DOI: https://doi.org/10.18692/1810-4800-2026-1-42-47
ABSTRACT
Abstract. Persistent allergic rhinitis (PAR) is a widespread chronic inflammatory disease of the upper respiratory tract, for which treatment often cannot achieve adequate symptom control, even when employing therapeutically recommended strategies by clinical protocols. This underscores the need for approaches that can adapt to the unique disease characteristics of each patient. The nasal microbiome plays a significant role in the pathogenesis of PAR and the modulation of inflammatory processes; however, its analysis is not yet integrated into clinical practice because of methodological limitations. In recent years, there has been a growing interest in employing artificial intelligence (AI) algorithms for microbiome data analysis, which opens up new possibilities for developing personalized therapeutic strategies. Objective. To study the relationship between nasal microbiome composition and clinical response to standard PAR therapy using the Random Forest algorithm. The algorithm was trained on 80% of the data and tested on 20%, which enabled the identification of significant associations between certain taxa (such as Staphylococcus aureus, Pseudomonas aeruginosa, Staphylococcus epidermidis and Corynebacterium spp.) and therapeutic outcomes. Results. The findings highlight the role of microbiome composition in predicting clinical outcomes and demonstrate the potential of AI as an analytical tool to uncover hidden connections in microbiome and clinical indicator data.
Publication date:
24.02.2026
Keywords:
persistent allergic rhinitis, nasal microbiome, artificial intelligence, personalized treatment, clinical outcome prediction? For citation:
Portenko E. G., Dobrynin K. B., Trigubenko R. A. Analysis of association between the nasal microbiome and treatment outcomes of persistent allergic rhinitis using artificial intelligence algorithms. Russian Otorhinolaryngology. 2026;25(1):42-47. (In Russ.) https://doi.org/10.18692/1810-4800-2026-1-42-47