人工智能在麻醉前评估中的应用研究进展
Research Progress on the Application of Artificial Intelligence in Pre-Anesthesia Assessment
DOI: 10.12677/acm.2024.14112867, PDF,   
作者: 付 蓉, 罗志锴*：延安大学医学院，陕西 延安；聂 煌：空军军医大学第一附属医院麻醉与围术期医学科，陕西 西安
关键词: 人工智能；机器学习；术前评估；Artificial Intelligence； Machine Learning； Preoperative Assessment
摘要: 随着科技的飞速发展，人工智能(Artificial intelligence, AI)已逐渐渗透到医学领域的各个角落，促进了医疗保健的发展。在麻醉学领域，人工智能的应用也逐渐扩展。麻醉前评估作为确保手术安全的重要环节，其准确性和效率直接关系到患者的生命健康。本文将详细探讨人工智能在麻醉前评估中的应用，以期为提高临床麻醉安全和效率提供新的思路和方法。
Abstract: With the rapid development of technology, Artificial Intelligence (AI) has gradually infiltrated various corners of the medical field, promoting the advancement of healthcare. In the field of anesthesiology, the application of AI is also gradually expanding. Pre-anesthesia assessment, as a crucial step in ensuring surgical safety, directly relates to the life and health of patients in terms of accuracy and efficiency. This article will delve into the application of AI in pre-anesthesia assessment, aiming to provide new ideas and methods for improving clinical anesthesia safety and efficiency.
文章引用：付蓉, 聂煌, 罗志锴. 人工智能在麻醉前评估中的应用研究进展[J]. 临床医学进展, 2024, 14(11): 221-226. https://doi.org/10.12677/acm.2024.14112867

参考文献

[1] Lopes, S., Rocha, G. and Guimarães-Pereira, L. (2023) Artificial Intelligence and Its Clinical Application in Anesthesiology: A Systematic Review. Journal of Clinical Monitoring and Computing, 38, 247-259. [Google Scholar] [CrossRef] [PubMed]
[2] Zaouter, C., Hemmerling, T.M., Lanchon, R., Valoti, E., Remy, A., Leuillet, S., et al. (2016) The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery. Anesthesia & Analgesia, 123, 885-893. [Google Scholar] [CrossRef] [PubMed]
[3] Tan, A.L., Chiew, C.J., Wang, S., Abdullah, H.R., Lam, S.S., Ong, M.E., et al. (2019) Risk Factors and Reasons for Cancellation within 24 H of Scheduled Elective Surgery in an Academic Medical Centre: A Cohort Study. International Journal of Surgery, 66, 72-78. [Google Scholar] [CrossRef] [PubMed]
[4] Duncan, D. and Wijeysundera, D.N. (2016) Preoperative Cardiac Evaluation and Management of the Patient Undergoing Major Vascular Surgery. International Anesthesiology Clinics, 54, 1-32. [Google Scholar] [CrossRef] [PubMed]
[5] Gebremedhn, E.G. and Nagaratnam, V. (2014) Assessment of Patient Satisfaction with the Preoperative Anesthetic Evaluation. Patient Related Outcome Measures, 5, 105-110. [Google Scholar] [CrossRef] [PubMed]
[6] 赵雪娇, 胡嘉乐, Beverly, G.G., 等. 成人全麻患者麻醉前评估临床实践指南分析[J]. 临床麻醉学杂志, 2018, 34(5): 463-467.
[7] Hussain, A.M. and Khan, F.A. (2005) Anaesthetic Reasons for Cancellation of Elective Surgical Inpatients on the Day of Surgery in a Teaching Hospital. JPMA: The Journal of the Pakistan Medical Association, 55, 374-378.
[8] Emanuel, A. and Macpherson, R. (2013) The Anaesthetic Pre-Admission Clinic Is Effective in Minimising Surgical Cancellation Rates. Anaesthesia and Intensive Care, 41, 90-94. [Google Scholar] [CrossRef] [PubMed]
[9] 张旭. 低碳概念下的建筑设计策略运用[J]. 工程管理与技术探讨, 2022, 4(1): 111-113.
[10] 陈珏, 侯旭敏, 娄洁琼, 等. 胸部疾病专科医院麻醉术前评估中心的建设与思考[J]. 中国医药导报, 2023, 20(6): 185-189.
[11] 朱斌, 汪云飞, 周祥勇, 等. 中国麻醉质量与患者安全调查[J]. 麻醉安全与质控, 2017, 1(2): 71-76.
[12] Apfelbaum, J.L., Connis, R.T., Nickinovich, D.G., et al. (2012) Practice Advisory for Preanesthesia Evaluation: An Updated Report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology, 116, 522-538.
[13] Pastides, P., Tokarczyk, S., Ismail, L., Ahearne, D. and Sarraf, K.M. (2011) Preoperative Blood Tests: An Expensive Tick Box Exercise. Journal of Perioperative Practice, 21, 421-424. [Google Scholar] [CrossRef] [PubMed]
[14] Phoenix, G.K., Elliott, T., Chan, J.K. and Das, S.K. (2012) Preoperative Blood Tests in Elective General Surgery: Cost and Clinical Implications. Journal of Perioperative Practice, 22, 282-288. [Google Scholar] [CrossRef] [PubMed]
[15] Kouki, P., Matsota, P., Christodoulaki, K., et al. (2012) Greek Surgical Patients’ Satisfaction Related to Perioperative Anesthetic Services in an Academic Institute. Patient Preference and Adherence, 6, 569-578. [Google Scholar] [CrossRef] [PubMed]
[16] Le May, S., Hardy, J., Taillefer, M. and Dupuis, G. (2001) Patient Satisfaction with Anesthesia Services. Canadian Journal of Anaesthesia, 48, 153-161. [Google Scholar] [CrossRef] [PubMed]
[17] 罗猛强, 王英伟. 人工智能麻醉系统的开发与应用[J]. 上海医学, 2023, 46(5): 272-277.
[18] 邵海军, 胡天然, 徐怡琼, 等. 人工智能与麻醉[J]. 上海医学, 2023, 46(5): 265-267.
[19] Zhang, L., Fabbri, D., Lasko, T.A., Ehrenfeld, J.M. and Wanderer, J.P. (2018) A System for Automated Determination of Perioperative Patient Acuity. Journal of Medical Systems, 42, Article No. 123. [Google Scholar] [CrossRef] [PubMed]
[20] Wongtangman, K., Aasman, B., Garg, S., Witt, A.S., Harandi, A.A., Azimaraghi, O., et al. (2023) Development and Validation of a Machine Learning Asa-Score to Identify Candidates for Comprehensive Preoperative Screening and Risk Stratification. Journal of Clinical Anesthesia, 87, Article ID: 111103. [Google Scholar] [CrossRef] [PubMed]
[21] Liu, R., Stone, T.A.D., Raje, P., Mather, R.V., Santa Cruz Mercado, L.A., Bharadwaj, K., et al. (2024) Development and Multicentre Validation of the FLEX Score: Personalised Preoperative Surgical Risk Prediction Using Attention-Based ICD-10 and Current Procedural Terminology Set Embeddings. British Journal of Anaesthesia, 132, 607-615. [Google Scholar] [CrossRef] [PubMed]
[22] 朱丹红, 郑辉哲, 何斌杰. 基于BP神经网络的肿瘤手术麻醉风险评估模型[J]. 中国现代医药杂志, 2019, 21(6): 10-12.
[23] Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P.M., et al. (2019) Mysurgeryrisk: Development and Validation of a Machine-Learning Risk Algorithm for Major Complications and Death after Surgery. Annals of Surgery, 269, 652-662. [Google Scholar] [CrossRef] [PubMed]
[24] Asai, T., Koga, K. and Vaughan, R.S. (1998) Respiratory Complications Associated with Tracheal Intubation and Extubation. British Journal of Anaesthesia, 80, 767-775. [Google Scholar] [CrossRef] [PubMed]
[25] Apfelbaum, J.L., Hagberg, C.A., Caplan, R.A., Blitt, C.D., Connis, R.T., Nickinovich, D.G., et al. (2013) Practice Guidelines for Management of the Difficult Airway. Anesthesiology, 118, 251-270. [Google Scholar] [CrossRef] [PubMed]
[26] Tobias, J.D. (2018) Preoperative Anesthesia Evaluation. Seminars in Pediatric Surgery, 27, 67-74. [Google Scholar] [CrossRef] [PubMed]
[27] Apfelbaum, J.L., Hagberg, C.A., Connis, R.T., Abdelmalak, B.B., Agarkar, M., Dutton, R.P., et al. (2021) 2022 American Society of Anesthesiologists Practice Guidelines for Management of the Difficult Airway. Anesthesiology, 136, 31-81. [Google Scholar] [CrossRef] [PubMed]
[28] Burgess, M.B., Schauer, S.G., Hood, R.L. and De Lorenzo, R.A. (2022) The Difficult Airway Redefined. Prehospital and Disaster Medicine, 37, 723-726. [Google Scholar] [CrossRef] [PubMed]
[29] Tavolara, T.E., Gurcan, M.N., Segal, S. and Niazi, M.K.K. (2021) Identification of Difficult to Intubate Patients from Frontal Face Images Using an Ensemble of Deep Learning Models. Computers in Biology and Medicine, 136, Article ID: 104737. [Google Scholar] [CrossRef] [PubMed]
[30] Wang, G., Li, C., Tang, F., Wang, Y., Wu, S., Zhi, H., et al. (2023) A Fully-Automatic Semi-Supervised Deep Learning Model for Difficult Airway Assessment. Heliyon, 9, e15629. [Google Scholar] [CrossRef] [PubMed]
[31] Xia, M., Jin, C., Zheng, Y., Wang, J., Zhao, M., Cao, S., et al. (2023) Deep Learning‐based Facial Analysis for Predicting Difficult Videolaryngoscopy: A Feasibility Study. Anaesthesia, 79, 399-409. [Google Scholar] [CrossRef] [PubMed]

为你推荐