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Chestnikova S.E.

Kursk State Medical University

Piskunov V.S.

Kursk State Medical University

Mezentseva O.Yu.

Kursk State Medical University

Rukavitsyn V.R.

Kursk State Medical University

Kolotovkina A.I.

Kursk State Medical University

Optimization of chronic rhinosinusitis diagnosis using artificial intelligence

Authors:

Chestnikova S.E., Piskunov V.S., Mezentseva O.Yu., Rukavitsyn V.R., Kolotovkina A.I.

More about the authors

Journal: Russian Rhinology. 2025;33(4): 38‑43

DOI: 10.17116/rosrino20253304138

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Table of contents

OPTIMIZATION OF CHRONIC RHINOSINUSITIS DIAGNOSIS USING ARTIFICIAL INTELLIGENCE
OPTIMIZATION OF CHRONIC RHINOSINUSITIS DIAGNOSIS USING ARTIFICIAL INTELLIGENCE

To cite this article:

Chestnikova SE, Piskunov VS, Mezentseva OYu, Rukavitsyn VR, Kolotovkina AI. Optimization of chronic rhinosinusitis diagnosis using artificial intelligence. Russian Rhinology. 2025;33(4):38‑43. (In Russ.)
https://doi.org/10.17116/rosrino20253304138

Подписка 2026 Российская ринология (Russian Rhinology)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

OBJECTIVE

To analyze the possibilities of artificial intelligence application in order to optimize the diagnosis of chronic rhinosinusitis.

MATERIAL AND METHODS

The review includes materials published in MEDLINE, PubMed information databases. The selection of material was carried out according to the following keywords: chronic rhinosinusitis, nasal polyps, artificial intelligence, machine learning, neural networks; with subsequent systematization in accordance with the task.

RESULTS

The review includes 18 publications that meet the inclusion criteria. The publications were devoted to the possibilities of artificial intelligence use in order to solve the problems of differential diagnosis of chronic rhinosinusitis endotypes, differential diagnosis of nasal polyps and inverted papillomas, detection of nasal polyps during endoscopy, segmentation of the paranasal sinuses, determination of the osteomeatal complex occlusion degree, differential diagnosis of fungal rhinosinusitis, assessment of sinus wall remodeling.

CONCLUSION

Software models created using machine learning have high accuracy and low operating costs, which will allow to use them for solution of the problems of comprehensive (clinical, radiation and endoscopic) diagnosis of chronic rhinosinusitis, simplifying the doctor’s work and reducing the frequency of expensive invasive techniques (for example, biopsy with subsequent pathohistological examination) application.

Keywords:

chronic rhinosinusitis
nasal polyps
artificial intelligence
machine learning
neural networks

Authors:

Chestnikova S.E.

Kursk State Medical University

Piskunov V.S.

Kursk State Medical University

Mezentseva O.Yu.

Kursk State Medical University

Rukavitsyn V.R.

Kursk State Medical University

Kolotovkina A.I.

Kursk State Medical University

Received:

13.02.2025

Accepted:

21.04.2025

Close metadata

References:

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