人工智能辅助围手术期麻醉管理干预措施：系统评价和荟萃分析。

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis.

机构信息

Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan.

Translational Research Promotion Center, Tsukuba Clinical Research & Development Organization, University of Tsukuba, Ibaraki, Japan.

出版信息

BMC Anesthesiol. 2024 Sep 4;24(1):306. doi: 10.1186/s12871-024-02699-z.

DOI:10.1186/s12871-024-02699-z

PMID:39232648

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11373311/

Abstract

BACKGROUND

Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727).

METHODS

We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023.

RESULTS

Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I 93.8%).

CONCLUSIONS

This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.

TRIAL REGISTRATION

This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

摘要

背景

人工智能（AI）在医疗实践中的应用最近有所增加。在麻醉学领域已经开发了许多 AI 模型，但它们在临床环境中的应用仍然有限。本研究旨在通过对随机对照试验进行系统评价和荟萃分析（CRD42022353727）来确定 AI 研究与其在麻醉学中的实施之间的差距。

方法

我们检索了医学文献分析与检索系统在线（MEDLINE）、医学文摘数据库（Embase）、科学网、考科兰临床试验中心注册库（CENTRAL）、电气和电子工程师协会 Xplore（IEEE）和谷歌学术，并检索了 2023 年 8 月 31 日之前在数据库成立日期和 2023 年 8 月 31 日之间发表的比较传统和 AI 辅助麻醉管理的随机对照试验。

结果

本系统评价共纳入 8 项随机对照试验（n=568 例患者），其中 286 例和 282 例患者分别接受了 AI 辅助干预和无 AI 辅助干预的麻醉管理。研究中使用的 AI 辅助干预措施包括气体浓度的模糊逻辑控制（一项研究）和低血压预测指数（七项研究；仅增加了一个指标）。七项研究的样本量较小（n=30 至 68，除最大研究外），包括最大样本量研究（n=213）的荟萃分析显示，与低血压相关的结局无差异（时间加权平均值的阈值 0.22 的差异，95%置信区间 -0.03 至 0.48，P=0.215，I 93.8%）。