机器学习在麻醉学领域的应用前景

机器学习（ML）技术已逐步被用于临床麻醉中，在围术期的应用及研究日益增多。ML在术前可以预警高危事件的发生，辅助困难气道的诊断以及超声显像；在术中可以预测低血压、低氧血症、心搏骤停以及麻醉深度等，帮助实现麻醉的精准和安全控制；在术后可以预测麻醉相关不良结局等。本文总结麻醉学领域常用的ML模型，回顾ML应用于围术期各个阶段的相关研究。ML的应用可改善围术期麻醉管理，有助于预警高危事件的发生，降低麻醉相关风险。

Application prospect of machine learning in field of anesthesiology

Machine learning (ML) technology has been gradually applied in clinical anesthesia, and the application and research in the perioperative period are increasing. ML can warn occurrence of high-risk events, assist the diagnosis of difficult airway and ultrasound imaging in the perioperative period. Intraoperatively, ML can predict hypotension, hypoxemia, cardiac arrest, and depth of anesthesia to help achieve precise and safe control of anesthesia. Postoperatively, ML can predict anesthesia-related adverse outcomes. This article summarizes the ML models commonly used in the field of anesthesiology, and reviews the relevant studies of ML application in all stages of the perioperative period. The application of ML can improve the perioperative anesthesia management, help to warn the occurrence of high-risk events and reduce anesthesia-related risks．