Zhong Mei, Yi Hao, Lai Fan, Liu Mujun, Zeng Rongdan, Kang Xue, Xiao Yahui, Rong Jingbo, Wang Huijin, Bai Jieyun, Lu Yaosheng
NanFang Hospital of Southern Medical University, Guangzhou 510515, China.
College of Information Science and Technology, Jinan University, Guangzhou 510632, China.
Matern Fetal Med. 2022 Apr 26;4(2):103-112. doi: 10.1097/FM9.0000000000000147. eCollection 2022 Apr.
This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.
A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired -test.
The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 2.0293 ± 0.9267, = -3.55 , = 0.004), Acc.F-measure (86.8562 ± 10.9422 72.2367 ± 14.2096, = 12.43, <0.001), Dec.F-measure (72.1038 ± 33.2592 58.5040 ± 38.0276, = 4.10, <0.001), SI (34.8277±20.9595 54.8049 ± 25.0265, = -9.39, <0.001), and MADI (3.1741 ± 1.9901 3.7289 ± 2.7253, = -2.74, = 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics.
The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.
本研究探讨基于人工智能分析胎儿心率（FHR）信号以获得基线计算结果，并通过分娩期间的电子胎儿监护识别FHR中的加速/减速的效果。
从南方医科大学南方医院收集了2012年1月至2020年12月期间分娩女性患者的43888份心电图（CTG）记录。对数据进行过滤后，使用2341份FHR记录进行研究。使用联影医疗科技有限公司生产的ObVue胎儿监护系统，从第一产程开始到分娩结束对这些孕妇的FHR信号进行监测。两位产科专家共同在系统中对FHR信号进行注释，以确定FHR的基线以及加速/减速情况。然后使用我们的心电图网络（CTGNet）以及传统方法自动分析FHR信号的基线和加速/减速情况。将计算结果与产科专家提供的注释进行比较，并应用十折交叉验证对其进行评估。基线之间的均方根差（RMSD）、加速F值（Acc.F-measure）、减速F值（Dec.F-measure）和形态分析不一致指数（MADI）用作评估指标。使用配对t检验对数据进行分析。
就RMSD.BL而言，所提出的CTGNet优于Mantel提出的最佳传统方法（1.7935±0.8099对2.0293±0.9267，t = -3.55，P = 0.004）、Acc.F-measure（86.8562±10.9422对72.2367±14.2096，t = 12.43，P <0.001）、Dec.F-measure（72.1038±33.2592对58.5040±38.0276，t = 4.10，P <0.001）、SI（34.8277±20.9595对54.8049±25.0265，t = -9.39，P <0.001）和MADI（3.1741±1.9901对3.7289±2.7253，t = -2.74，P = 0.012）。因此，所提出的CTGNet在所有评估指标上均比最佳传统方法具有显著优势。
所提出的基于人工智能的方法CTGNet在基于心电图数据自动分析FHR方面表现良好。它有望成为未来智能产科系统的关键组成部分。
