ArticleMaternal-fetal medicine (Wolters Kluwer Health, Inc.)2022
CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence.
Article in Maternal-fetal medicine (Wolters Kluwer Health, Inc.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Review
- Fetal Health Prediction From Cardiotocography Recordings Using Kolmogorov-Arnold Networks.IEEE open journal of engineering in medicine and biology · 2025Article
- Automated analysis of fetal heart rate baseline/acceleration/deceleration using MTU-Net3 + model.Biomedical engineering letters · 2024Article
- Editorial: New technologies improve maternal and newborn safety.Frontiers in medical technology · 2024Article
- Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data.Health information science and systems · 2023Article
- Comparison of fetal heart rate baseline estimation by the cardiotocograph network and clinicians: a multidatabase retrospective assessment study.Frontiers in cardiovascular medicine · 2023Article
- Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings.Biosensors · 2022Article
- How synergy between mechanistic and statistical models is impacting research in atrial fibrillation.Frontiers in physiology · 2022Review
- A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system.Digital healthArticle
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11 authors.
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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 Results: The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 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.
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