Comparison of a novel computerized analysis program and visual interpretation of cardiotocography

Large-scale analysis of interobserver agreement and reliability in cardiotocography interpretation during labor using an online tool

BACKGROUND

While the effectiveness of cardiotocography in reducing neonatal morbidity is still debated, it remains the primary method for assessing fetal well-being during labor. Evaluating how accurately professionals interpret cardiotocography signals is essential for its effective use. The objective was to evaluate the accuracy of fetal hypoxia prediction by practitioners through the interpretation of cardiotocography signals and clinical variables during labor.

MATERIAL AND METHODS

We conducted a cross-sectional online survey, involving 120 obstetric healthcare providers from several countries. One hundred cases, including fifty cases of fetal hypoxia, were randomly assigned to participants who were invited to predict the fetal outcome (binary criterion of pH with a threshold of 7.15) based on the cardiotocography signals and clinical variables. After describing the participants, we calculated (with a 95% confidence interval) the success rate, sensitivity and specificity to predict the fetal outcome for the whole population and according to pH ranges, professional groups and number of years of experience. Interobserver agreement and reliability were evaluated using the proportion of agreement and Cohen's kappa respectively.

RESULTS

The overall ability to predict a pH level below 7.15 yielded a success rate of 0.58 (95% CI 0.56-0.60), a sensitivity of 0.58 (95% CI 0.56-0.60) and a specificity of 0.63 (95% CI 0.61-0.65). No significant difference in the success rates was observed with respect to profession and number of years of experience. The success rate was higher for the cases with a pH level below 7.05 (0.69) and above 7.20 (0.66) compared to those falling between 7.05 and 7.20 (0.48). The proportion of agreement between participants was good (0.82), with an overall kappa coefficient indicating substantial reliability (0.63).

CONCLUSIONS

The use of an online tool enabled us to collect a large amount of data to analyze how practitioners interpret cardiotocography data during labor. Despite a good level of agreement and reliability among practitioners, the overall accuracy is poor, particularly for cases with a neonatal pH between 7.05 and 7.20. Factors such as profession and experience level do not present notable impact on the accuracy of the annotations. The implementation and use of a computerized cardiotocography analysis software has the potential to enhance the accuracy to detect fetal hypoxia, especially for ambiguous cardiotocography tracings.

Analysis of the transplacental transmission of SARS CoV-2 virus and antibody transfer according to the gestational age at maternal infection

To quantify transplacental transmission of SARS-CoV-2 virus and antibody transfer in pregnant women and their newborns according to the gestational age at maternal infection. A prospective observational multicenter study including pregnant women with a positive RT-PCR or a positive serology for SARS-CoV-2 and compatible symptoms, from April to December 2020, in 11 French maternities. The study was designed to obtain a systematic collection of mother-infant dyad's samples at birth. SARS-CoV-2 viral load was measured by RT-PCR. IgG and IgM antibodies against the SARS-CoV-2 spike protein were measured by enzyme-linked immunosorbent assay. Antibody concentrations and transplacental transfer ratios were analyzed according to the gestational age at maternal infection. The primary outcome was the rate of SARS CoV-2 materno-fetal transmission at birth. The secondary outcome was the quantification of materno-fetal antibody transfer. Maternal and neonatal outcomes at birth were additionally assessed. Among 165 dyads enrolled, one congenital infection was confirmed {n = 1 (0.63%) IC95% [0.02%; 3.48%]}. The average placental IgG antibody transfer ratio was 1.27 (IC 95% [0.69-2.89]). The transfer ratio increased with increasing time between the onset of maternal infection and delivery (P Value = 0.0001). Maternal and neonatal outcomes were reassuring. We confirmed the very low rate of SARS-CoV-2 transplacental transmission (< 1%). Maternal antibody transfer to the fetus was more efficient when the infection occurred during the first and second trimester of pregnancy.

Comparison of fetal heart rate baseline estimation by the cardiotocograph network and clinicians: a multidatabase retrospective assessment study

Background

This study aims to compare the fetal heart rate (FHR) baseline predicted by the cardiotocograph network (CTGNet) with that estimated by clinicians.

Material and methods

A total of 1,267 FHR recordings acquired with different electrical fetal monitors (EFM) were collected from five datasets: 84 FHR recordings acquired with F15 EFM (Edan, Shenzhen, China) from the Guangzhou Women and Children's Medical Center, 331 FHR recordings acquired with SRF618B5 EFM (Sanrui, Guangzhou, China), 234 FHR recordings acquired with F3 EFM (Lian-Med, Guangzhou, China) from the NanFang Hospital of Southen Medical University, 552 cardiotocographys (CTG) recorded using STAN S21 and S31 (Neoventa Medical, Mölndal, Sweden) and Avalon FM40 and FM50 (Philips Healthcare, Amsterdam, The Netherlands) from the University Hospital in Brno, Czech Republic, and 66 FHR recordings acquired using Avalon FM50 fetal monitor (Philips Healthcare, Amsterdam, The Netherlands) at St Vincent de Paul Hospital (Lille, France). Each FHR baseline was estimated by clinicians and CTGNet, respectively. And agreement between CTGNet and clinicians was evaluated using the kappa statistics, intra-class correlation coefficient, and the limits of agreement.

Results

The number of differences <3 beats per minute (bpm), 3-5 bpm, 5-10 bpm and ≥10 bpm, is 64.88%, 15.94%, 14.44% and 4.74%, respectively. Kappa statistics and intra-class correlation coefficient are 0.873 and 0.969, respectively. Limits of agreement are -6.81 and 7.48 (mean difference: 0.36 and standard deviation: 3.64).

Conclusion

An excellent agreement was found between CTGNet and clinicians in the baseline estimation from FHR recordings with different signal loss rates.

Artificial intelligence and machine learning in cardiotocography: A scoping review

INTRODUCTION

Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability.

MATERIAL AND METHODS

The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed.

RESULTS

We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice.

CONCLUSIONS

There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.

Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep

To overcome the difficulties in interpreting fetal heart rate (FHR), several tools based on the autonomic nervous system and heart rate variability (HRV) have been developed. The objective of this study was to use FHR and HRV parameters for the prediction of fetal hypoxia. It was an experimental study in the instrumented fetal sheep. Repeated umbilical cord occlusions were performed to achieve severe acidosis. Hemodynamic parameters, ECG, and blood gases were analyzed. The variables used were heart rate baseline, HRV analysis (RMSSD, SDNN, LF, HF, HFnu, Fetal Stress Index (FSI), …), and morphological analysis of decelerations. The gold standard used to classify hypoxia was the fetal arterial pH (pH < 7.10). Different multivariable statistical methods (logistic regression and decision trees) were applied for the detection of acidosis. 21 lambs were instrumented. A total of 130 pairs of FHR/fetal pH analysis were obtained of which 29 in the acidosis group and 101 in the non-acidosis group. After logistic regression model with bootstrap resampling and stepwise backward selection, only one variable was selected, FSI. The AUC of FSI alone in this model was 0.81 with a sensitivity of 0.66, specificity of 0.88, PPV of 0.61, and NPV of 0.90 considering a threshold of 68. Decision trees with CHAID and CART algorithms showed a sensitivity of 0.48 and 0.59, respectively, and a specificity of 0.94 for both. All employed methods identified HRV variables as the most predictive of acidosis. The primary variables selected automatically were those from the HRV. Supporting the use of FHRV measures for the screening of fetal acidosis during labour is interesting.

A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

CONTEXT

The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns.

OBJECTIVE

This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG.

METHODS

Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers.

RESULTS

After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles).

DISCUSSION

This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques.

CONCLUSIONS

This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis

OBJECTIVES

To determine the degree of inter-rater reliability (IRR) between human and artificial intelligence (AI) interpretation of fetal heart rate tracings (FHR), and to determine whether AI-assisted electronic fetal monitoring interpretation improves neonatal outcomes amongst laboring women.

DATA SOURCES

We searched Medline, EMBASE, Google Scholar, Scopus, ISI Web of Science and Cochrane database search, as well as PubMed ( www.pubmed.gov ) and RCT registry ( www.clinicaltrials.gov ) until the end of October 2018 to conduct a systematic review and meta-analysis comparing visual and AI interpretation of EFM in labor. Similarly, we sought out all studies evaluating the IRR between AI and expert interpretation of EFM.

TABULATION, INTEGRATION AND RESULTS

Weighed mean Cohen's Kappa was calculated to assess the global IRR. Risk of bias was assessed using the Cochrane Handbook for Systematic Reviews of Interventions. We used relative risks (RR) and a random effects (RE) model to calculate weighted estimates. Statistical homogeneity was checked by the χ² test and I² using Review Manager 5.3.5 (The Cochrane Collaboration, 2014.) We obtained 201 records, of which 9 met inclusion criteria. Three RCT's were used to compare the neonatal outcomes and 6 cohort studies were used to establish the degree of IRR between both approaches of EFM evaluation. With regards to the neonatal outcomes, a total of 55,064 patients were included in the analysis. Relative to the use of clinical (visual) evaluation of the FHR, the use of AI did not change the incidence rates of neonatal acidosis, cord pH below < 7.20, 5-min APGAR scores < 7, mode of delivery, NICU admission, neonatal seizures, or perinatal death. With regards to the degrees of inter-rater reliability, a weighed mean Cohen's Kappa of 0.49 [0.32-0.66] indicates moderate agreement between expert observers and computerized systems.

CONCLUSION

The use of AI and computer analysis for the interpretation of EFM during labor does not improve neonatal outcomes. Inter-rater reliability between experts and computer systems is moderate at best. Future studies should aim at further elucidating these findings.

Cardiotocography and the evolution into computerised cardiotocography in the management of intrauterine growth restriction

Timely recognition and appropriate management of high-risk pregnancies, such as intrauterine growth restriction (IUGR), are of paramount importance for every obstetrician. After the initial screening of IUGR fetuses through sonographic fetometry and Doppler, the focus is shifted to the appropriate monitoring and timing of delivery. This can, especially in cases of early IUGR, become a very difficult task. At this point, cardiotocography (CTG) is introduced as a major tool in the day-to-day monitoring of the antenatal well-being of the IUGR fetus. Since the first introduction of CTG up to the nowadays widely spreading implementation of computerised CTG in the clinical practice, there has been great progress in the recording of the fetal heart rate, as well as its interpretation. Focus of this review is to offer an understanding of the evolution of CTG from its early development to modern computerised methods and to provide an insight as to where the future of CTG is leading, especially in the monitoring of IUGR.