Viderman Dmitriy, Ayazbay Ainur, Kalzhan Bakhtiyar, Bayakhmetova Symbat, Tungushpayev Meiram, Abdildin Yerkin
Department of Surgery, School of Medicine, Nazarbayev University, 010000 Astana, Kazakhstan.
Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, 010000 Astana, Kazakhstan.
J Clin Med. 2024 Dec 11;13(24):7535. doi: 10.3390/jcm13247535.
Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs. This scoping review aimed to consolidate and evaluate the existing evidence for the application of AI in managing respiratory failure among patients necessitating MV. : The literature search was conducted in PubMed, Scopus, and the Cochrane Library. Studies investigating the utilization of AI in patients undergoing MV, including observational and randomized controlled trials, were selected. : Overall, 152 articles were screened, and 37 were included in the analysis. We categorized the goals of AI in the included studies into the following groups: (1) prediction of requirement in MV; (2) prediction of outcomes in MV; (3) prediction of weaning from MV; (4) prediction of hypoxemia after extubation; (5) prediction models for MV-associated severe acute kidney injury; (6) identification of long-term outcomes after prolonged MV; (7) prediction of survival. : AI has been studied in a wide variety of patients with respiratory failure requiring MV. Common applications of AI in MV included the assessment of the performance of ML for mortality prediction in patients with respiratory failure, prediction and identification of the most appropriate time for extubation, detection of patient-ventilator asynchrony, ineffective expiration, and the prediction of the severity of the respiratory failure.
机械通气(MV)是重症监护病房(ICU)中最常用的器官替代方式之一。人工智能(AI)在优化机械通气管理方面具有巨大潜力。AI在MV中的作用在于其能够利用电子监测系统的大量数据,促进根据个体患者需求提供个性化护理。本综述旨在汇总和评估AI在需要MV的患者呼吸衰竭管理中应用的现有证据。:在PubMed、Scopus和Cochrane图书馆进行文献检索。选择研究AI在接受MV的患者中的应用的研究,包括观察性研究和随机对照试验。:总体而言,筛选了152篇文章,37篇纳入分析。我们将纳入研究中AI的目标分为以下几组:(1)MV需求预测;(2)MV结果预测;(3)MV撤机预测;(4)拔管后低氧血症预测;(5)MV相关严重急性肾损伤预测模型;(6)长期MV后长期结局识别;(7)生存预测。:AI已在各种需要MV的呼吸衰竭患者中进行了研究。AI在MV中的常见应用包括评估机器学习在呼吸衰竭患者死亡率预测中的性能、预测和确定最合适的拔管时间、检测患者-呼吸机不同步、无效呼气以及呼吸衰竭严重程度的预测。