CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence. Issue 2 (26th April 2022)

• Record Type: Journal Article
• Authors: Zhong, Mei; Yi, Hao; Lai, Fan; Liu, Mujun; Zeng, Rongdan; Kang, Xue; Xiao, Yahui; Rong, Jingbo; Wang, Huijin; Bai, Jieyun; Lu, Yaosheng
• Editors: Pan, Yang
• Is Part Of: Maternal-fetal medicine. Volume 4:Issue 2(2022)
• Page Start: 103
• Page End: 112
• Publication Date: 2022-04-26
• Subjects: Artificial intelligence -- Deep learning -- Smart obstetrics -- Fetal heart rate -- Cardiotocograph -- Baseline -- Acceleration -- Deceleration; Fetal Diseases; Fetus -- physiology; Pregnancy -- physiology; Pregnancy Complications; Electronic journals; Periodical; 618.3005
• DOI: 10.1097/FM9.0000000000000147
• Languages: English
• ISSNs: 2096-6954

Abstract

Abstract: Objective: 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. Methods: 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 t -test. Results: The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t = −3.55, P = 0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t = 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t = 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t = −9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t = −2.74, P = 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion: 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.