基于人工智能的气道管理优化策略与实践分析

赵美玉; 王明亚; 韩永正; 郭向阳; 贾斐

doi:10.13406/j.cnki.cyxb.003693

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重庆医科大学学报 ›› 2025, Vol. 50 ›› Issue (01) : 1-5. DOI: 10.13406/j.cnki.cyxb.003693

综述

基于人工智能的气道管理优化策略与实践分析

赵美玉 ¹ ,
王明亚 ¹ ,
韩永正 ¹ ,
郭向阳 ¹ ,
贾斐 ²

作者信息 +

Optimization strategy and practice analysis of airway management based on artificial intelligence

Author information +

History +

摘要

气道管理是临床麻醉学、急救医学和重症医学领域的重要技术之一，妥善的气道管理可有效减少患者术后肺部并发症、降低患者的死亡风险。人工智能在麻醉学领域的应用日臻成熟，已成为现代麻醉实践的重要组成部分。根据医学影像信息识别困难气道的算法模型可为复杂气道评估提供辅助决策。当前麻醉机器人在气道管理领域的技术革新进展显著，已发展出基于机器视觉技术和深度神经网络技术的机器人辅助气管插管系统。本文将重点介绍人工智能时代麻醉机器人在气道管理中的应用进展。

Abstract

Airway management plays an important role in clinical anesthesiology，emergency medicine，and critical care medicine. Proper airway management can effectively reduce postoperative pulmonary complications and lower the risk of death for patients. The application of artificial intelligence in the field of anesthesiology is becoming increasingly mature and has become an important component of modern anesthesia practice. The algorithm model that identifies difficult airway by analyzing medical imaging information can provide decision support for assessing complex airway. The technological innovation of anesthesia robots in airway management has made significant progress，and a robot-assisted tracheal intubation system based on machine vision technology and deep neural network technology has been developed. This review focuses on the application progress of anesthesia robots in airway management in the era of artificial intelligence.

关键词

气管插管 / 机器人 / 人工智能 / 机器学习 / 深度学习

Key words

tracheal intubation / robot / artificial intelligence / machine learning / deep learning

中图分类号

R614 / R782.05

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导出引用

赵美玉 , 王明亚 , 韩永正 , 等. 基于人工智能的气道管理优化策略与实践分析. 重庆医科大学学报. 2025, 50(01): 1-5 https://doi.org/10.13406/j.cnki.cyxb.003693

Zhao Meiyu, Wang Mingya, Han Yongzheng, et al. Optimization strategy and practice analysis of airway management based on artificial intelligence[J]. Journal of Chongqing Medical University. 2025, 50(01): 1-5 https://doi.org/10.13406/j.cnki.cyxb.003693

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

1	Russotto V， Myatra SN， Laffey JG，et al. Intubation practices and adverse peri-intubation events in critically ill patients from 29 countries[J]. JAMA，2021，325（12）：1164-1172. 本文引用 [1]

2	Apfelbaum JL， Hagberg CA， Connis RT，et al. 2022 American society of anesthesiologists practice guidelines for management of the difficult airway[J]. Anesthesiology，2022，136（1）：31-81. 本文引用 [1]

3	Cook TM， Woodall N， Frerk C，et al. Major complications of airway management in the UK：results of the Fourth National Audit Project of the Royal College of Anaesthetists and the Difficult Airway Society. Part 1：anaesthesia[J]. Br J Anaesth，2011，106（5）：617-631. 本文引用 [1]

4	Simpson GD， Ross MJ， McKeown DW，et al. Tracheal intubation in the critically ill：a multi-centre national study of practice and complications[J]. Br J Anaesth，2012，108（5）：792-799. 本文引用 [1]

5	Natt BS， Malo J， Hypes CD，et al. Strategies to improve first attempt success at intubation in critically ill patients[J]. Br J Anaesth，2016，117（Suppl 1）：i60-i68.

6	Jung W， Kim J. Factors associated with first-pass success of emergency endotracheal intubation[J]. Am J Emerg Med，2020，38（1）：109-113. 本文引用 [1]

7	Sakles JC， Chiu S， Mosier J，et al. The importance of first pass success when performing orotracheal intubation in the emergency department[J]. Acad Emerg Med，2013，20（1）：71-78. 本文引用 [1]

8	Higgs A， McGrath BA， Goddard C，et al. Guidelines for the management of tracheal intubation in critically ill adults[J]. Br J Anaesth，2018，120（2）：323-352. 本文引用 [1]

9	Trent SA， Kaji AH， Carlson JN，et al. Video laryngoscopy is associated with first-pass success in emergency department intubations for trauma patients：a propensity score matched analysis of the national emergency airway registry[J]. Ann Emerg Med，2021，78（6）：708-719. 本文引用 [1]

10	Tavolara TE， Gurcan MN， Segal S，et al. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models[J]. Comput Biol Med，2021，136：104737. 本文引用 [1]

11	Carlson JN， Das S， de la Torre F，et al. A novel artificial intelligence system for endotracheal intubation[J]. Prehosp Emerg Care，2016，20（5）：667-671. 本文引用 [1]

12	Xia M， Jin C， Zheng Y，et al. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study [J]. Anaesthesia，2024，79（4）：399-409. 本文引用 [1]

13	Parker F， Brodsky MB， Akst LM，et al. Machine learning in laryngoscopy analysis：a proof of concept observational study for the identification of post-extubation ulcerations and granulomas[J]. Ann Otol Rhinol Laryngol，2021，130（3）：286-291. 本文引用 [1]

14	Hayasaka T， Kawano K， Kurihara K，et al. Creation of an artificial intelligence model for intubation difficulty classification by deep learning （convolutional neural network） using face images：an observational study[J]. J Intensive Care，2021，9（1）：38. 本文引用 [1]

15	Cho HY， Lee K， Kong HJ，et al. Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view[J]. Anaesthesia，2023，78（1）：64-72. 本文引用 [1]

16	Wang GZ， Li CX， Tang FD，et al. A fully-automatic semi-supervised deep learning model for difficult airway assessment[J]. Heliyon，2023，9（5）：e15629. 本文引用 [1]

17	Li Y， Zheng XX， Xie FF，et al. Development and validation of the artificial intelligence （AI）-based diagnostic model for bronchial lumen identification[J]. Transl Lung Cancer Res，2022，11（11）：2261-2274. 本文引用 [1]

18	Choi SJ， Kim DK， Kim BS，et al. Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope[J]. Digit Health，2023，9：20552076231211547. 本文引用 [1]

19	吕志峰，方　洁，王洋，等. 基于深度学习算法可视喉镜下模拟人声门图像识别模型的构建[J]. 中华麻醉学杂志，2023，43（6）：723-727. Lv ZF， Fang J， Wang Y，et al. Construction of three image recognition models of manikin’s glottis using visual laryngoscopy based on deep-learning algorithm[J]. Chin J Anesthesiol，2023，43（6）：723-727. 本文引用 [1]

20	Mazzeo AT， La Monaca E， Di Leo R，et al. Heart rate variability：a diagnostic and prognostic tool in anesthesia and intensive care[J]. Acta Anaesthesiol Scand，2011，55（7）：797-811. 本文引用 [1]

21	Shaffer F， McCraty R， Zerr CL. A healthy heart is not a metronome：an integrative review of the heart’s anatomy and heart rate variability[J]. Front Psychol，2014，5：1040. 本文引用 [1]

22	Gruson D， Bernardini S， Dabla PK，et al. Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine[J]. Clin Chim Acta，2020，509：67-71. 本文引用 [1]

23	Yin Q， Shen D， Tang Y，et al. Intelligent monitoring of noxious stimulation during anaesthesia based on heart rate variability analysis[J]. Comput Biol Med，2022，145：105408. 本文引用 [1]

24	Shah SB， Chawla R， Kaur C. Assessment of stress response due to C-Mac D-blade guided videolaryngoscopic endotracheal intubation and docking of da Vinci surgical robot using perfusion index in patients undergoing transoral robotic oncosurgery[J]. J Clin Monit Comput，2023，37（4）：1011-1021. 本文引用 [1]

25	Tighe PJ， Badiyan SJ， Luria I，et al. Robot-assisted airway support：a simulated case[J]. Anesth Analg，2010，111（4）：929-931. 本文引用 [1]

26	Hemmerling TM， Taddei R， Wehbe M，et al. First robotic tracheal intubations in humans using the Kepler intubation system[J]. Br J Anaesth，2012，108（6）：1011-1016. 本文引用 [1]

27	Cheng XY， Jiang GJ， Lee K，et al. IntuBot：design and prototyping of a robotic intubation device[C]//2018 IEEE International Conference on Robotics and Automation （ICRA）. Brisbane，QLD，Australia. IEEE，2018：1482-1487. 本文引用 [1]

28	Wang XY， Tao YF， Tao XD，et al. An original design of remote robot-assisted intubation system[J]. Sci Rep，2018，8（1）：13403. 本文引用 [1]

29	Biro P， Hofmann P， Gage D，et al. Automated tracheal intubation in an airway manikin using a robotic endoscope：a proof of concept study[J]. Anaesthesia，2020，75（7）：881-886. 本文引用 [1]

30	王荣峰，张倩雲，丁泓帆，等. 基于磁导航技术的气管插管装置设计及可行性研究[J]. 中国医疗器械杂志，2021，45（1）：22-25. Wang RF， Zhang QY， Ding HF，et al. Design and feasibility study of tracheal intubation device based on magnetic navigation technology[J]. Chin J Med Instrum，2021，45（1）：22-25. 本文引用 [1]

31	Dhananchey R， Henri Jean Alinier G， Bouras A，et al. Conceptualization of artificial intelligence in airway management[J]. J Emerg Med Trauma Acute Care，2022，2022（1）：1-2. 本文引用 [1]

32	Deng Z， Zhang SZ， Guo YX，et al. Assisted teleoperation control of robotic endoscope with visual feedback for nasotracheal intubation[J]. Robot Auton Syst，2024，172：104586. 本文引用 [1]

33	任伟东，吕　楠，李梦杰，等. 人工智能在气道管理中的应用[J]. 临床麻醉学杂志，2024，40（3）：304-309. Ren WD， Lyu N， Li MJ，et al. Application of artificial intelligence in airway management[J]. J Clin Anesthesiol，2024，40（3）：304-309. 本文引用 [1]

基金

国家自然科学基金面上资助项目(82071189)

首都卫生发展专项资助项目(首发2024-2-40912)

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Received	Published
2024-04-23	2025-01-28
Issue Date
2025-01-28

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