Heterogeneity in management of Category II fetal tracings: data from a multi-hospital healthcare system

Thayer, Sydney M.; Faramarzi, Parisa; Krauss, Melissa J.; Snider, Elsa; Kelly, Jeannie C.; Carter, Ebony B.; Frolova, Antonina I.; Odibo, Anthony O.; Raghuraman, Nandini

doi:10.1016/j.ajog.2021.11.1026

Cited by 1 publication

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“…On the one hand, FHR analysis is easily affected by the doctor's experience and skill, leading to low reproducibility of diagnoses owing to high inter- and intra- operator variability. On the other hand, it is time-consuming to analyze FHR signals for longer than 20 min, where this significantly reduces diagnostic efficiency 16,17 …”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, it is timeconsuming to analyze FHR signals for longer than 20 min, where this significantly reduces diagnostic efficiency. 16,17 To deal with the above problems, many researchers have proposed algorithms for automated FHR analysis and claimed that the results are better than those of the consensus of human experts in terms of the consistency of interpretation of FHR. 4,15,18,19 Algorithms for automated FHR analysis 20 first determine the baseline FHR and then detect episodes of Acc and Dec. Studies have shown that if the FHR baseline can be accurately calculated, all abnormal episodes except for sinusoidal patterns can be located easily and accurately.…”

Section: Introductionmentioning

confidence: 99%

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CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence

Zhong

Hao

Fan

et al. 2022

Maternal-Fetal Medicine

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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), coefficient of synthetic inconsistency (SI) 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 = À3.55 , P = 0.004),

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Section: Introductionmentioning

confidence: 99%