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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

Views: 499

Downloaded: 3

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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

 
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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

List of references:

  1. Paderno A, Gennarini F, Sordi A, Montenegro C, Lancini D, Villani FP, Moccia S, Piazza C. Artificial intelligence in clinical endoscopy: Insights in the field of videomics. Frontiers in Surgery. 2022;9:933297. https://orcid.org/10.3389/fsurg.2022.933297
  2. Cao C, Liu F, Tan H, Song D, Shu W, Li W, Zhou Y, Bo X, Xie Z. Deep Learning and Its Applications in Biomedicine. Genomics Proteomics Bioinformatics. 2018;16(1):17-32.  https://doi.org/10.1016/j.gpb.2017.07.003
  3. Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval. 2022;11(1):19-38.  https://doi.org/10.1007/s13735-021-00218-1
  4. Paches AI. Opukholi golovy i shei: klinicheskoe rukovodstvo. 5-e izd., dop. i pererab. M.: Prakticheskaya meditsina; 2013. (In Russ.).
  5. Cheremisina OV, Choinzonov EL. Potentials of endoscopic diagnosis of precencer diseases and cancer of the larynx. Sibirskij onkologicheskij zhurnal. 2007;3(23):5-9. (In Russ.). https://cyberleninka.ru/article/n/vozmozhnosti-endoskopicheskoy-diagnostiki-predopuholevyh-zabolevaniy-i-raka-gortani-v-sovremennoy-onkologii/viewer
  6. Cho WK, Lee YJ, Joo HA, Jeong IS, Choi Y, Nam SY, Kim SY, Choi SH. Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network-Based Image Classification System. Laryngoscope. 2021;131(11):2558-2566. https://doi.org/10.1002/lary.29595
  7. Larsen C.F., Pedersen M. Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images. European Archives of Oto-Rhino-Laryngology. 2023;280:2365-2371. https://doi.org/10.1007/s00405-022-07736-6
  8. Esmaeili N, Sharaf E, Gomes Ataide EJ, Illanes A, Boese A, Davaris N, Arens C, Navab N, Friebe M. Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging. Sensors (Basel). 2021;21(23):8157. https://orcid.org/10.3390/s21238157
  9. Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q, Ma L, Sun Y, Xu W, Yang N, Zou J, Zheng Y, Chen M, Gan W, Xiang T, An J, Liu R, Lv C, Lin K, Zheng X, Lou F, Rao Y, Yang H, Liu K, Liu G, Lu T, Zheng X, Zhao Y. Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020;130(11):E686-E693. https://orcid.org/10.1002/lary.28539
  10. Xiong H, Lin P, Yu JG, Ye J, Xiao L, Tao Y, Jiang Z, Lin W, Liu M, Xu J, Hu W, Lu Y, Liu H, Li Y, Zheng Y, Yang H. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. eBioMedicine. 2019;48:92-99.  https://orcid.org/10.1016/j.ebiom.2019.08.075
  11. Azam MA, Sampieri C, Ioppi A, Africano S, Vallin A, Mocellin D, Fragale M, Guastini L, Moccia S, Piazza C, Mattos LS, Peretti G. Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection. Laryngoscope. 2022;132(9):1798-1806. https://orcid.org/10.1002/lary.29960
  12. Hu R, Zhong Q, Xu ZG, Huang LY, Cheng Y, Wang YR, He YD, Cheng Y. Application of deep convolutional neural networks in the diagnosis of laryngeal squamous cell carcinoma based on narrow band imaging endoscopy. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2021;56(5):454-458.  https://orcid.org/10.3760/cma.j.cn115330-20200927-00773

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