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Potential of a neural network in the diagnosis of laryngeal tumors
- 作者: 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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隶属关系:
- The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
- The Russian National Research Medical University named after N.I. Pirogov
- Rubedo LLC
- 期: 卷 5, 编号 1S (2024)
- 页面: 98-101
- 栏目: 青年科学家的文章
- ##submission.dateSubmitted##: 16.02.2024
- ##submission.dateAccepted##: 05.03.2024
- ##submission.datePublished##: 03.07.2024
- URL: https://jdigitaldiagnostics.com/DD/article/view/627076
- DOI: https://doi.org/10.17816/DD627076
- ID: 627076
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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.
全文:
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.
作者简介
Evgeniya Safyannikova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: zh.saffi@inbox.ru
SPIN 代码: 9016-9253
俄罗斯联邦, Moscow
Andrey 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 代码: 9393-8753
俄罗斯联邦, Moscow; Moscow
Natalya 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 代码: 9282-6970
俄罗斯联邦, Moscow; Moscow
Pavel Sudarev
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: mnpco@mail.ru
ORCID iD: 0000-0001-9085-9879
SPIN 代码: 4113-3569
俄罗斯联邦, Moscow
Svetlana Romanenko
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: s_romanenko@bk.ru
ORCID iD: 0000-0002-8202-5505
SPIN 代码: 5645-3401
俄罗斯联邦, Moscow
Diana Kurbanova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
编辑信件的主要联系方式.
Email: doctor_diana@mail.ru
ORCID iD: 0000-0002-3571-8851
SPIN 代码: 4597-5197
Scopus 作者 ID: 55589484800
Researcher ID: AFE-6792-2022
俄罗斯联邦, Moscow
Ekaterina Lesogorova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: katenan1@rambler.ru
ORCID iD: 0000-0003-1753-5960
SPIN 代码: 1602-4311
俄罗斯联邦, Moscow
Ekaterina Krasil’nikova
The Sverzhevskiy Otorhinolaryngology Healthcare Research Institute
Email: fil_kate@mail.ru
ORCID iD: 0000-0002-8675-078X
SPIN 代码: 8759-7139
俄罗斯联邦, Moscow
Anastasiya Ivanova
Rubedo LLC
Email: AnastasiaIwanova@yandex.ru
ORCID iD: 0009-0001-4684-5864
俄罗斯联邦, Moscow
Anton Osadchiy
Rubedo LLC
Email: uhogorlonosiki@yandex.ru
ORCID iD: 0009-0001-3270-4390
俄罗斯联邦, Moscow
Natalya Shevyrina
Rubedo LLC
Email: shevyrina.nata22@gmail.com
ORCID iD: 0009-0003-9446-5457
俄罗斯联邦, Moscow
参考
- Paderno A, Gennarini F, Sordi A, et al. Artificial intelligence in clinical endoscopy: Insights in the field of videomics. Frontiers in Surgery. 2022;9:933297. doi: 10.3389/fsurg.2022.933297
- Cao C, Liu F, Tan H, et al. Deep Learning and Its Applications in Biomedicine. Genomics Proteomics Bioinformatics. 2018;16(1):17–32. doi: 10.1016/j.gpb.2017.07.003
- 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. doi: 10.1007/s13735-021-00218-1
- Paches AI, Brzhezovskii VZh, Demidov LV, et al. Head and neck tumors. Moscow: Prakticheskaya meditsina; 2013. (In Russ). EDN: XXRBCO
- Cheremisina OV, Choinzonov EL. Potentials of endoscopic diagnosis of precancer diseases and cancer of the larynx. Siberian journal of oncology. 2007;3(23):5–9. EDN: IBAIOD
- Ren J, Jing X, Wang J, et al. Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020;130(11):E686–E693. doi: 10.1002/lary.28539
- Xiong H, Lin P, Yu JG, et al. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine. 2019;48:92–99. doi: 10.1016/j.ebiom.2019.08.075
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