Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks

Abstract

Background: Cardiotocography is a common method of electronic fetal monitoring (EFM) for fetal well-being. Data-driven analyses have shown potential for automated EFM assessment. For this preliminary study, we used a novel artificial intelligence method based on fully convolutional networks (FCNs), with deep learning for EFM evaluation and correct recognition, and its possible role in evaluation of nonreassuring fetal status. Methods: We retrospectively collected 3239 EFM labor records from 292 deliveries and neonatal Apgar scores between December 2018 and July 2019 at a single medical center. We analyzed these data using an FCN model and compared the results with clinical practice. Results: The FCN model recognized EFM traces like physicians, with an average Cohen’s kappa coefficient of agreement of 0.525 and average area under the receiver operating characteristic curve of 0.892 for six fetal heart rate (FHR) categories. The FCN model showed higher sensitivity for predicting fetal compromise (0.528 vs 0.132) but a higher false-positive rate (0.632 vs 0.012) compared with clinical practice. Conclusion: FCN is a modern technique that may be useful for EFM trace recognition based on its multiconvolutional layered analysis. Our model showed a competitive ability to identify FHR patterns and the potential for evaluation of nonreassuring fetal status.

Artificial intelligence in obstetrics

“…Fetal heart rate monitoring leads to a decrease in neonatal seizures; however, it is associated with an increase in C-sections and instrumental vaginal birth rates. A recent study has shown that artificial intelligence algorithms based on fully convolutional networks can predict non reassuring fetal heart rate patterns with an accuracy of AUC 0.892 [ 61 ]. There are also reports of successful abnormal fetal heart rate pattern and fetal electrocardiogram prediction using deep learning [ 62 ].…”

Outstanding research paper awards of the Journal of the Chinese Medical Association in 2021

“…A big congratulation is given to Dr. Li-Chun Liu as the 2021 Journal of the Chinese Medical Association Outstanding Research Paper Award winner who is selected from all authors having contributed their research works to the Journal of the Chinese Medical Association (JCMA) last year. [1][2][3][4][5][6] This year's award, sponsored by the Chinese Medical Association-Taipei (CMA-Taipei), is granted to researchers who demonstrate their excellent performance and great contribution to enhanced better patients' care. Dr. Liu won this credit at the Annual Meeting of the CMA-Taipei on July 16, 2022, held at Taipei, Taiwan, and the meeting is still affected apparently by the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (coronavirus disease 2019 [COVID-19]) pandemic.…”

Winners of the 2021 honor awards for excellence at the annual meeting of the Chinese Medical Association-Taipei: Part I

“…The first winner is Dr. Liu who won the CMA-Taipei Outstanding Research Work Award for her publication, entitled “Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks.” 1 The detailed introduction has been shown before. 2 In brief, they used a novel artificial intelligence (AI) tool with a combination of fully convolutional networks and deep learning to provide a chance to monitor fetal well-being (fetal heart rate) and adequate uterine contraction during labor continuously and precisely.…”