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人工智能在产时胎儿心率(FHR)监测图解读中的应用:系统评价和荟萃分析。

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.

机构信息

Department of Obstetrics and Gynecology, McGill University, Montreal, QC, Canada.

出版信息

Arch Gynecol Obstet. 2019 Jul;300(1):7-14. doi: 10.1007/s00404-019-05151-7. Epub 2019 May 3.

Abstract

OBJECTIVES

To determine the degree of inter-rater reliability (IRR) between human and artificial intelligence (AI) interpretation of fetal heart rate tracings (FHR), and to determine whether AI-assisted electronic fetal monitoring interpretation improves neonatal outcomes amongst laboring women.

DATA SOURCES

We searched Medline, EMBASE, Google Scholar, Scopus, ISI Web of Science and Cochrane database search, as well as PubMed ( www.pubmed.gov ) and RCT registry ( www.clinicaltrials.gov ) until the end of October 2018 to conduct a systematic review and meta-analysis comparing visual and AI interpretation of EFM in labor. Similarly, we sought out all studies evaluating the IRR between AI and expert interpretation of EFM.

TABULATION, INTEGRATION AND RESULTS: Weighed mean Cohen's Kappa was calculated to assess the global IRR. Risk of bias was assessed using the Cochrane Handbook for Systematic Reviews of Interventions. We used relative risks (RR) and a random effects (RE) model to calculate weighted estimates. Statistical homogeneity was checked by the χ test and I using Review Manager 5.3.5 (The Cochrane Collaboration, 2014.) We obtained 201 records, of which 9 met inclusion criteria. Three RCT's were used to compare the neonatal outcomes and 6 cohort studies were used to establish the degree of IRR between both approaches of EFM evaluation. With regards to the neonatal outcomes, a total of 55,064 patients were included in the analysis. Relative to the use of clinical (visual) evaluation of the FHR, the use of AI did not change the incidence rates of neonatal acidosis, cord pH below < 7.20, 5-min APGAR scores < 7, mode of delivery, NICU admission, neonatal seizures, or perinatal death. With regards to the degrees of inter-rater reliability, a weighed mean Cohen's Kappa of 0.49 [0.32-0.66] indicates moderate agreement between expert observers and computerized systems.

CONCLUSION

The use of AI and computer analysis for the interpretation of EFM during labor does not improve neonatal outcomes. Inter-rater reliability between experts and computer systems is moderate at best. Future studies should aim at further elucidating these findings.

摘要

目的

确定人类和人工智能(AI)对胎儿心率图(FHR)的解释之间的观察者间一致性(IRR)程度,并确定 AI 辅助电子胎儿监测解释是否能改善产妇分娩时的新生儿结局。

资料来源

我们检索了 Medline、EMBASE、Google Scholar、Scopus、ISI Web of Science 和 Cochrane 数据库搜索,以及 PubMed(www.pubmed.gov)和 RCT 注册处(www.clinicaltrials.gov),截至 2018 年 10 月底,进行了一项系统评价和荟萃分析,比较了劳动中视觉和 AI 对 EFM 的解释。同样,我们寻找了所有评估 AI 与 EFM 专家解释之间 IRR 的研究。

列表、综合和结果:使用加权 Cohen's Kappa 评估全球 IRR。使用 Cochrane 干预系统评价手册评估偏倚风险。我们使用相对风险(RR)和随机效应(RE)模型计算加权估计值。使用 Review Manager 5.3.5(Cochrane 协作,2014 年)检查统计同质性。我们获得了 201 条记录,其中 9 条符合纳入标准。三项 RCT 用于比较新生儿结局,6 项队列研究用于确定两种 EFM 评估方法之间的 IRR 程度。关于新生儿结局,共有 55064 名患者纳入分析。与使用临床(视觉)评估 FHR 相比,使用 AI 并未改变新生儿酸中毒、脐带 pH 值<7.20、5 分钟 APGAR 评分<7、分娩方式、NICU 入院、新生儿癫痫发作或围产儿死亡的发生率。关于观察者间一致性程度,加权平均 Cohen's Kappa 为 0.49[0.32-0.66],表明专家观察者和计算机系统之间存在中度一致性。

结论

在分娩期间使用 AI 和计算机分析解释 EFM 并不能改善新生儿结局。专家和计算机系统之间的 IRR 最多为中度。未来的研究应旨在进一步阐明这些发现。

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