A review of the current status and progress in difficult airway assessment research

被引:10
作者
Chen, Haoming [1 ,2 ,3 ]
Zheng, Yuqi [1 ,2 ]
Fu, Qiang [4 ]
Li, Peng [1 ,2 ,3 ,5 ]
机构
[1] Univ Elect Sci & Technol China, Sichuan Acad Med Sci, Dept Anesthesiol, Chengdu, Peoples R China
[2] Univ Elect Sci & Technol China, Sichuan Prov Peoples Hosp, Chengdu, Peoples R China
[3] Southwest Med Univ, Luzhou, Peoples R China
[4] Third Peoples Hosp Chengdu, Dept Anesthesiol, Chengdu, Peoples R China
[5] First Peoples Hosp Guangyuan, Dept Anesthesiol, Guangyuan, Peoples R China
关键词
Difficult airway; Difficult intubation; Evaluation; Artificial intelligence; Machine learning; Deep learning; Application; TRACHEAL INTUBATION; ARTIFICIAL-INTELLIGENCE; DIRECT LARYNGOSCOPY; ADULT PATIENTS; IMAGE-ANALYSIS; PREDICTION; MANAGEMENT; ENDOSCOPY; MODEL; TOOL;
D O I
10.1186/s40001-024-01759-x
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
A difficult airway is a situation in which an anesthesiologist with more than 5 years of experience encounters difficulty with intubation or mask ventilation. According to the 2022 American Society of Anesthesiologists Practice Guidelines for the Management of Difficult Airway, difficult airways are subdivided into seven detailed categories. This condition can lead to serious adverse events and therefore must be diagnosed accurately and quickly. In this review, we comprehensively summarize and discuss the different methods used in clinical practice and research to assess difficult airways, including medical history, simple bedside assessment, comprehensive assessment of indicators, preoperative endoscopic airway examination, imaging, computer-assisted airway reconstruction, and 3D-printing techniques. We also discuss in detail the latest trends in difficult airway assessment through mathematical methods and artificial intelligence. With the continuous development of artificial intelligence and other technologies, in the near future, we will be able to predict whether a patient has a difficult airway simply by taking an image of the patient's face through a cell phone program. Artificial intelligence and other technologies will bring great changes to the development of airway assessment, and at the same time raise some new questions that we should think about.
引用
收藏
页数:9
相关论文
共 67 条
[11]   Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation [J].
Cuendet, Gabriel Louis ;
Schoettker, Patrick ;
Yuece, Anil ;
Sorci, Matteo ;
Gao, Hua ;
Perruchoud, Christophe ;
Thiran, Jean-Philippe .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2016, 63 (02) :328-339
[12]   Upper lip bite test for prediction of difficult airway: A systematic review [J].
Faramarzi, Elnaz ;
Soleimanpour, Hassan ;
Khan, Zahid Hussain ;
Mahmoodpoor, Ata ;
Sanaie, Sarvin .
PAKISTAN JOURNAL OF MEDICAL SCIENCES, 2018, 34 (04) :1019-1023
[13]   Deep learning for healthcare applications based on physiological signals: A review [J].
Faust, Oliver ;
Hagiwara, Yuki ;
Hong, Tan Jen ;
Lih, Oh Shu ;
Acharya, U. Rajendra .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 161 :1-13
[14]   Difficult Airway Society 2015 guidelines for management of unanticipated difficult intubation in adults [J].
Frerk, C. ;
Mitchell, V. S. ;
McNarry, A. F. ;
Mendonca, C. ;
Bhagrath, R. ;
Patel, A. ;
O'Sullivan, E. P. ;
Woodall, N. M. ;
Ahmad, I. .
BRITISH JOURNAL OF ANAESTHESIA, 2015, 115 (06) :827-848
[15]   Pre-operative transnasal endoscopy as a predictor of difficult airway A prospective cohort study [J].
Gemma, Marco ;
Buratti, Luca ;
Di Santo, Davide ;
Calvi, Maria R. ;
Ravizza, Alfredo ;
Bondi, Stefano ;
Bussi, Mario ;
Beretta, Luigi .
EUROPEAN JOURNAL OF ANAESTHESIOLOGY, 2020, 37 (02) :98-104
[16]   Three-dimensional printing as an aid to airway evaluation after tracheotomy in a patient with laryngeal carcinoma [J].
Han, Bin ;
Liu, Yajie ;
Zhang, Xiaoqing ;
Wang, Jun .
BMC ANESTHESIOLOGY, 2016, 16
[17]   Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study [J].
Hayasaka, Tatsuya ;
Kawano, Kazuharu ;
Kurihara, Kazuki ;
Suzuki, Hiroto ;
Nakane, Masaki ;
Kawamae, Kaneyuki .
JOURNAL OF INTENSIVE CARE, 2021, 9 (01)
[18]   Mathematical modelling and prediction in infectious disease epidemiology [J].
Huppert, A. ;
Katriel, G. .
CLINICAL MICROBIOLOGY AND INFECTION, 2013, 19 (11) :999-1005
[19]   Diagnostic accuracy of radiology (CT, X-ray, US) for predicting difficult intubation in adults: A meta-analysis [J].
Ji, Chao ;
Ni, Qiang ;
Chen, Wurong .
JOURNAL OF CLINICAL ANESTHESIA, 2018, 45 :79-87
[20]   Emerging role of deep learning-based artificial intelligence in tumor pathology [J].
Jiang, Yahui ;
Yang, Meng ;
Wang, Shuhao ;
Li, Xiangchun ;
Sun, Yan .
CANCER COMMUNICATIONS, 2020, 40 (04) :154-166