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Kryukov A.I.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Sudarev P.A.

Sverzhevsky Research Institute of Clinical Otorhinolaryngology Moscow Department of Healthcare

Romanenko S.G.

Sverzhevky Research Clinical Institute of Otorhinolaryngology

Kurbanova D.I.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Lesogorova E.V.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Krasilnikova E.N.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Pavlikhin O.G.

L.I. Sverzhevskiy Research and Clinical Institute of Otorhinolaryngology of the Moscow Healthcare Department

Ivanova A.A.

Rubedo LLC

Osadchiy A.P.

Rubedo LLC

Shevyrina N.G.

Rubedo LLC

Diagnosis of benign laryngeal tumors using neural network

Authors:

Kryukov A.I., Sudarev P.A., Romanenko S.G., Kurbanova D.I., Lesogorova E.V., Krasilnikova E.N., Pavlikhin O.G., Ivanova A.A., Osadchiy A.P., Shevyrina N.G.

More about the authors

Journal: Russian Bulletin of Otorhinolaryngology. 2024;89(3): 24‑28

DOI: 10.17116/otorino20248903124

Read: 1151 times

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Table of contents

DIAGNOSIS OF BENIGN LARYNGEAL TUMORS USING NEURAL NETWORK
DIAGNOSIS OF BENIGN LARYNGEAL TUMORS USING NEURAL NETWORK

To cite this article:

Kryukov AI, Sudarev PA, Romanenko SG, et al. . Diagnosis of benign laryngeal tumors using neural network. Russian Bulletin of Otorhinolaryngology. 2024;89(3):24‑28. (In Russ.)
https://doi.org/10.17116/otorino20248903124

Подписка 2026 Вестник оториноларингологии (Russian Bulletin of Otorhinolaryngology)
 
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

The article describes our experience in developing and training an artificial neural network based on artificial intelligence algorithms for recognizing the characteristic features of benign laryngeal tumors and variants of the norm of the larynx based on the analysis of laryngoscopy pictures obtained during the examination of patients. During the preparation of data for training the neural network, a dataset was collected, labeled and loaded, consisting of 1471 images of the larynx in digital formats (jpg, bmp). Next, the neural network was trained and tested in order to recognize images of the norm and neoplasms of the larynx. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing of benign laryngeal tumors and variants of the norm of the larynx. The proposed technology can be further used in practical healthcare to control and improve the quality of diagnosis of laryngeal pathologies.

Keywords:

larynx
benign laryngeal tumor
vocal fold normality
neural network
artificial intelligence

Authors:

Kryukov A.I.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Sudarev P.A.

Sverzhevsky Research Institute of Clinical Otorhinolaryngology Moscow Department of Healthcare

Romanenko S.G.

Sverzhevky Research Clinical Institute of Otorhinolaryngology

Kurbanova D.I.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Lesogorova E.V.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Krasilnikova E.N.

Sverzhevsky Research Clinical Institute of Otorhinolaryngology

Pavlikhin O.G.

L.I. Sverzhevskiy Research and Clinical Institute of Otorhinolaryngology of the Moscow Healthcare Department

Ivanova A.A.

Rubedo LLC

Osadchiy A.P.

Rubedo LLC

Shevyrina N.G.

Rubedo LLC

Received:

05.09.2023

Accepted:

01.03.2024

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References:

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