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2
Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review.人工智能在办公喉镜检查中的应用:范围综述。
Laryngoscope. 2022 Oct;132(10):1993-2016. doi: 10.1002/lary.29886. Epub 2021 Sep 28.
3
Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.深度学习在声门疾病预测中的应用:通过语音识别——初步开发研究
J Med Internet Res. 2021 Jun 8;23(6):e25247. doi: 10.2196/25247.
4
[Application of deep convolutional neural networks in the diagnosis of laryngeal squamous cell carcinoma based on narrow band imaging endoscopy].基于窄带成像内镜的深度卷积神经网络在喉鳞状细胞癌诊断中的应用
Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2021 May 7;56(5):454-458. doi: 10.3760/cma.j.cn115330-20200927-00773.
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Deep Neural Network for Automatic Classification of Pathological Voice Signals.深度神经网络在病理嗓音信号自动分类中的应用。
J Voice. 2022 Mar;36(2):288.e15-288.e24. doi: 10.1016/j.jvoice.2020.05.029. Epub 2020 Jul 10.
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Laryngopharyngeal reflux image quantization and analysis of its severity.喉咽反流图像的量化及其严重程度分析。
Sci Rep. 2020 Jul 3;10(1):10975. doi: 10.1038/s41598-020-67587-1.
7
Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.光学活检:使用卷积神经网络对气道内镜检查结果进行自动分类。
Laryngoscope. 2022 Feb;132 Suppl 4:S1-S8. doi: 10.1002/lary.28708. Epub 2020 Apr 28.
8
Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.使用深度学习技术自动识别喉镜图像。
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9
Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.基于喉镜图像的深度学习辅助喉癌计算机辅助诊断。
EBioMedicine. 2019 Oct;48:92-99. doi: 10.1016/j.ebiom.2019.08.075. Epub 2019 Oct 5.
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Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning.使用深度学习对基于智能手机的双模态口腔发育异常和恶性肿瘤图像进行自动分类。
Biomed Opt Express. 2018 Oct 10;9(11):5318-5329. doi: 10.1364/BOE.9.005318. eCollection 2018 Nov 1.

语音分析与内镜技术联合人工智能在咽喉疾病诊疗中的应用与发展

[Application and development of voice analysis and endoscopic technology combined with artificial intelligence in the diagnosis and treatment of throat disease].

作者信息

Song Qi, Li Xiaoming

机构信息

Department of Otolaryngology Head and Neck Surgery,the 980th Hospital of the Joint Logistics Support Unit of the Chinese PLA,Shijiazhuang,050082,China.

出版信息

Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2022 Aug;36(8):647-650. doi: 10.13201/j.issn.2096-7993.2022.08.017.

DOI:10.13201/j.issn.2096-7993.2022.08.017
PMID:35959588
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10128196/
Abstract

In the diagnosis and treatment of throat disease, the application and development of combining voice analysis or endoscopic technology with artificial intelligence has developed rapidly. This paper reviews the history and principles of the combination of voice analysis or endoscopic technology with artificial intelligence, summarizes its status of application and development, and sums up its advantages that lie in the strong learning and interpretation ability, amazing speed and tolerance, and stable replication and expansion. The key to restrict its development is the uncertainty in the process of machine learning, the error caused by small samples, and the ethical philosophical thinking. Future development direction should be that the surgeons in otolaryngology head and neck department on the basis of excellent professional knowledge, learn related knowledge of epidemiology, classic statistics, strengthen the exchanges and cooperation with machine learning developers. Eventually, advanced science and technology can be truly used in clinical practice to maximize the benefit of the majority of patients.

摘要

在咽喉疾病的诊断与治疗中,语音分析或内镜技术与人工智能相结合的应用与发展迅速。本文回顾了语音分析或内镜技术与人工智能相结合的历史与原理,总结了其应用与发展现状,并归纳了其优势,即强大的学习与解读能力、惊人的速度与耐受性以及稳定的复制与扩展能力。限制其发展的关键在于机器学习过程中的不确定性、小样本导致的误差以及伦理哲学思考。未来的发展方向应该是耳鼻咽喉头颈外科医生在具备优秀专业知识的基础上,学习流行病学、经典统计学等相关知识,加强与机器学习开发者的交流与合作。最终,先进科技能够真正应用于临床实践,使广大患者受益最大化。