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Potential of a neural network in the diagnosis of laryngeal tumors
- Authors: Safyannikova E.A.1, Kryukov A.I.1,2, Kunelskaya N.L.1,2, Sudarev P.A.1, Romanenko S.G.1, Kurbanova D.I.1, Lesogorova E.V.1, Krasil’nikova E.N.1, Ivanova A.A.3, Osadchiy A.P.3, Shevyrina N.G.3
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Affiliations:
- The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
- The Russian National Research Medical University named after N.I. Pirogov
- Rubedo LLC
- Issue: Vol 5, No 1S (2024)
- Pages: 98-101
- Section: Articles by YOUNG SCIENTISTS
- Submitted: 16.02.2024
- Accepted: 05.03.2024
- Published: 03.07.2024
- URL: https://jdigitaldiagnostics.com/DD/article/view/627076
- DOI: https://doi.org/10.17816/DD627076
- ID: 627076
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Abstract
BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7].
AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality.
MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks.
Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses.
RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms.
CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.
Full Text
BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7].
AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality.
MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks.
Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses.
RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms.
CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.
About the authors
Evgeniya A. Safyannikova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: zh.saffi@inbox.ru
ORCID iD: 0000-0005-7780-6488
SPIN-code: 9016-9253
Russian Federation, Moscow
Andrey I. Kryukov
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute; The Russian National Research Medical University named after N.I. Pirogov
Email: nikio@zdrav.mos.ru
ORCID iD: 0000-0002-0149-0676
SPIN-code: 9393-8753
Russian Federation, Moscow; Moscow
Natalya L. Kunelskaya
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute; The Russian National Research Medical University named after N.I. Pirogov
Email: nlkun@mail.ru
ORCID iD: 0000-0002-1001-2609
SPIN-code: 9282-6970
Russian Federation, Moscow; Moscow
Pavel A. Sudarev
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: mnpco@mail.ru
ORCID iD: 0000-0001-9085-9879
SPIN-code: 4113-3569
Russian Federation, Moscow
Svetlana G. Romanenko
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: s_romanenko@bk.ru
ORCID iD: 0000-0002-8202-5505
SPIN-code: 5645-3401
Russian Federation, Moscow
Diana I. Kurbanova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: doctor_diana@mail.ru
ORCID iD: 0000-0002-3571-8851
SPIN-code: 4597-5197
Scopus Author ID: 55589484800
ResearcherId: AFE-6792-2022
Russian Federation, Moscow
Ekaterina V. Lesogorova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: katenan1@rambler.ru
ORCID iD: 0000-0003-1753-5960
SPIN-code: 1602-4311
Russian Federation, Moscow
Ekaterina N. Krasil’nikova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: fil_kate@mail.ru
ORCID iD: 0000-0002-8675-078X
SPIN-code: 8759-7139
Russian Federation, Moscow
Anastasiya A. Ivanova
Rubedo LLC
Email: AnastasiaIwanova@yandex.ru
ORCID iD: 0009-0001-4684-5864
Russian Federation, Moscow
Anton P. Osadchiy
Rubedo LLC
Email: uhogorlonosiki@yandex.ru
ORCID iD: 0009-0001-3270-4390
Russian Federation, Moscow
Natalya G. Shevyrina
Rubedo LLC
Author for correspondence.
Email: shevyrina.nata22@gmail.com
ORCID iD: 0009-0003-9446-5457
Russian Federation, Moscow
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