Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance.

BACKGROUND AND OBJECTIVE: Obstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for the collection and annotation of CTG signals. Previous research has demonstrated that multicenter studies can improve model performance. However, models trained on cross-domain data may not generalize well to target domains due to variance in distribution among datasets. Hence, this paper conducted a multicenter study with Deep Semi-Supervised Domain Adaptation (DSSDA) for intelligent interpretation of antenatal CTG signals. This approach helps to align cross-domain distribution and transfer knowledge from a label-rich source domain to a label-scarce target domain. METHODS: We proposed a DSSDA framework that integrated Minimax Entropy and Domain Invariance (DSSDA-MMEDI) to reduce inter-domain gaps and thus achieve domain invariance. The networks were developed using GoogLeNet to extract features from CTG signals, with fully connected, softmax layers for classification. We designed a Dynamic Gradient-driven strategy based on Mutual Information (DGMI) to unify the losses from Minimax Entropy (MME), Domain Invariance (DI), and supervised cross-entropy during iterative learning. RESULTS: We validated our DSSDA model on two datasets collected from collaborating healthcare institutions and mobile terminals as the source and target domains, which contained 16,355 and 3,351 CTG signals, respectively. Compared to the results achieved with deep learning networks without DSSDA, DSSDA-MMEDI significantly improved sensitivity and F1-score by over 6%. DSSDA-MMEDI also outperformed other state-of-the-art DSSDA approaches for CTG signal interpretation. Ablation studies were performed to determine the unique contribution of each component in our DSSDA mechanism. CONCLUSIONS: The proposed DSSDA-MMEDI is feasible and effective for alignment of cross-domain data and automated interpretation of multicentric antenatal CTG signals with minimal annotation cost.

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review.

INTRODUCTION: Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. MATERIALS AND METHOD: We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. RESULTS: We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. CONCLUSION: ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.

Midwives' attitudes toward the use of cardiotocograph (CTG) machines in labor units: A study in Jordan.

BACKGROUND: The purpose of cardiotocograph (CTG) usage is to detect any alterations in fetal heart rate (FHR) early before they are prolonged and profound. However, the use of CTG machines on a routine basis is not an evidence-supported practice. There is no Jordanian study that assesses the midwives' attitudes toward this machine. This study aimed to identify Jordanian midwives' attitudes towards the use of cardiotocograph (CTG) machines in labor units, alongside examining the relationships between midwives' personal sociodemographic characteristics and such attitudes. METHODS: A descriptive research design was used to identify Jordanian midwives' attitudes towards the use of CTG machines in both public and private labor units in Jordan. Data were collected using the valid and reliable tool designed by Sinclair (2001), and these were used to identify midwives' attitudes towards CTG usage. A total of 329 midwives working in the labor units of governmental and private hospitals in the center and north of Jordan participated in the study from May to July 2022. RESULTS: The total mean score for the attitude scale was M = 3.14 (SD = 0.83). More than half of the sample (N = 187, 58.4 %) demonstrated a mean score greater than 3.14, however, which indicates generally positive attitudes toward CTG usage in labor units. Midwives working in private hospitals and those holding Bachelor's degrees had more positive attitudes toward the use of CTG machines. CONCLUSION: This study provides new insights into the attitudes of Jordanian midwives towards CTG use in labor units. These suggest that it is critical to conduct training courses for registered midwives to help them develop and/or regain confidence and competence with respect to various key aspects of intrapartum care, including intermittent auscultation and the appropriate use of CTG.

Fetal Heart Rate Auscultation, 4th Edition.

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

Artificial intelligence based cardiotocogram assessment during labor.

OBJECTIVE: To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events. STUDY DESIGN: A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation. RESULTS: The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations. CONCLUSION: The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice.

Fetal Heart Rate Auscultation, 4th Edition.

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA.

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.

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

INTRODUCTION: In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. MATERIAL AND METHODS: Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. RESULTS: A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. CONCLUSIONS: Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Intrapartum cardiotocography with simultaneous maternal heart rate registration improves neonatal outcome.

BACKGROUND: Intrapartum cardiotocographic monitoring of fetal heart rate by abdominal external ultrasound transducer without simultaneous maternal heart rate recording has been associated with increased risk of early neonatal death and other asphyxia-related neonatal outcomes. It is unclear, however, whether this increase in risk is independently associated with fetal surveillance method or is attributable to other factors. OBJECTIVE: This study aimed to compare different fetal surveillance methods and their association with adverse short- and long-term fetal and neonatal outcomes in a large retrospective cohort of spontaneous term deliveries. STUDY DESIGN: Fetal heart rate and maternal heart rate patterns were recorded by cardiotocography during labor in spontaneous term singleton cephalic vaginal deliveries in the Hospital District of Helsinki and Uusimaa, Finland between October 1, 2005, and September 30, 2023. According to the method of cardiotocography monitoring at birth, the cohort was divided into the following 3 groups: women with ultrasound transducer, women with both ultrasound transducer and maternal heart rate transducer, and women with internal fetal scalp electrode. Umbilical artery pH and base excess values, low 1- and 5-minute Apgar scores, need for intubation and resuscitation, neonatal intensive care unit admission for asphyxia, neonatal encephalopathy, and early neonatal death were used as outcome variables. RESULTS: Among the 213,798 deliveries that met the inclusion criteria, the monitoring type was external ultrasound transducer in 81,559 (38.1%), both external ultrasound transducer and maternal heart rate recording in 62,268 (29.1%), and fetal scalp electrode in 69,971 (32.7%) cases, respectively. The rates of both neonatal encephalopathy (odds ratio, 1.48; 95% confidence interval, 1.08-2.02) and severe acidemia (umbilical artery pH <7.00 and/or umbilical artery base excess ≤-12.0 mmol/L) (odds ratio, 2.03; 95% confidence interval, 1.65-2.50) were higher in fetuses of women with ultrasound transducer alone compared with those of women with concurrent external fetal and maternal heart rate recording. Monitoring with ultrasound transducer alone was also associated with increased risk of neonatal intubation for resuscitation (odds ratio, 1.22; 95% confidence interval, 1.03-1.44). A greater risk of severe neonatal acidemia was observed both in the ultrasound transducer (odds ratio, 2.78; 95% confidence interval, 2.23-3.48) and concurrent ultrasound transducer and maternal heart rate recording (odds ratio, 1.37; 95% confidence interval, 1.05-1.78) groups compared with those monitored with fetal scalp electrodes. No difference in risk of neonatal encephalopathy was found between newborns monitored with concurrent ultrasound transducer and maternal heart rate recording and those monitored with fetal scalp electrodes. CONCLUSION: The use of external ultrasound transducer monitoring of fetal heart rate without simultaneous maternal heart rate recording is associated with higher rates of neonatal encephalopathy and severe neonatal acidemia. We suggest that either external fetal heart rate monitoring with concurrent maternal heart rate recording or internal fetal scalp electrode be used routinely as a fetal surveillance tool in term deliveries.

ST waveform analysis vs cardiotocography alone for intrapartum fetal monitoring: An updated systematic review and meta-analysis of randomized trials.

INTRODUCTION: ST waveform analysis (STAN) was introduced as an adjunct to cardiotocography (CTG) to improve neonatal and maternal outcomes. The aim of the present study was to quantify the efficacy of STAN vs CTG and assess the quality of the evidence using GRADE. MATERIAL AND METHODS: We performed systematic literature searches to identify randomized controlled trials and assessed included studies for risk of bias. We performed meta-analyses, calculating pooled risk ratio (RR) or Peto odds ratio (OR). We also performed post hoc trial sequential analyses for selected outcomes to assess the risk of false-positive results and the need for additional studies. RESULTS: Nine randomized controlled trials including 28 729 women were included in the meta-analysis. There were no differences between the groups in operative deliveries for fetal distress (10.9 vs 11.1%; RR 0.96; 95% confidence interval [CI] 0.82-1.11). STAN was associated with a significantly lower rate of metabolic acidosis (0.45% vs 0.68%; Peto OR 0.66; 95% CI 0.48-0.90). Accordingly, 441 women need to be monitored with STAN instead of CTG alone to prevent one case of metabolic acidosis. Women allocated to STAN had a reduced risk of fetal blood sampling compared with women allocated to conventional CTG monitoring (12.5% vs 19.6%; RR 0.62; 95% CI 0.49-0.80). The quality of the evidence was high to moderate. CONCLUSIONS: Absolute effects of STAN were minor and the clinical significance of the observed reduction in metabolic acidosis is questioned. There is insufficient evidence to state that STAN as an adjunct to CTG leads to important clinical benefits compared with CTG alone.

Intrapartum fetal heart rate monitoring: Rationalise, refine or replace?

Monitoring the fetal heartbeat underpins assessment of fetal wellbeing in labour. Although commonly employed in clinical practice, shortcomings remain. A recent review of clinical practice guidelines highlights the variation in definitions of the fetal heart rate that will lead to differences in interpretation. Will intrapartum care be improved by greater consensus around clinical practice guidelines through rationalisation or refinement of guidelines, or will the future see this technique replaced by more accurate forms of fetal monitoring?

Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring: An interobserver agreement comparison with traditional classification.

INTRODUCTION: It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring. MATERIAL AND METHODS: A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ). RESULTS: The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%-52%) and for FFTree 60% (95% CI 56-64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71-77) and 70% (95% CI 67-74). For "intervention needed" the figures were 52% (95% CI 47-56) vs 58% (95% CI 54-62) and for "expectant management" 74% (95% CI 71-77) vs 72% (95% CI 69-75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44-0.56) for the FIGO classification and 0.57 (95% CI 0.51-0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47-0.59) and 0.57 (95% CI 0.51-0.63). CONCLUSIONS: The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.

Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination.

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk.

Why is safety in intrapartum electronic fetal monitoring so hard? A qualitative study combining human factors/ergonomics and social science analysis.

BACKGROUND: Problems in intrapartum electronic fetal monitoring with cardiotocography (CTG) remain a major area of preventable harm. Poor understanding of the range of influences on safety may have hindered improvement. Taking an interdisciplinary perspective, we sought to characterise the everyday practice of CTG monitoring and the work systems within which it takes place, with the goal of identifying potential sources of risk. METHODS: Human factors/ergonomics (HF/E) experts and social scientists conducted 325 hours of observations and 23 interviews in three maternity units in the UK, focusing on how CTG tasks were undertaken, the influences on this work and the cultural and organisational features of work settings. HF/E analysis was based on the Systems Engineering Initiative for Patient Safety 2.0 model. Social science analysis was based on the constant comparative method. RESULTS: CTG monitoring can be understood as a complex sociotechnical activity, with tasks, people, tools and technology, and organisational and external factors all combining to affect safety. Fetal heart rate patterns need to be recorded and interpreted correctly. Systems are also required for seeking the opinions of others, determining whether the situation warrants concern, escalating concerns and mobilising response. These processes may be inadequately designed or function suboptimally, and may be further complicated by staffing issues, equipment and ergonomics issues, and competing and frequently changing clinical guidelines. Practice may also be affected by variable standards and workflows, variations in clinical competence, teamwork and situation awareness, and the ability to communicate concerns freely. CONCLUSIONS: CTG monitoring is an inherently collective and sociotechnical practice. Improving it will require accounting for complex system interdependencies, rather than focusing solely on discrete factors such as individual technical proficiency in interpreting traces.

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities.

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection.

Fetal hypoxia can cause damaging consequences on babies' such as stillbirth and cerebral palsy. Cardiotocography (CTG) has been used to detect intrapartum fetal hypoxia during labor. It is a non-invasive machine that measures the fetal heart rate and uterine contractions. Visual CTG suffers inconsistencies in interpretations among clinicians that can delay interventions. Machine learning (ML) showed potential in classifying abnormal CTG, allowing automatic interpretation. In the absence of a gold standard, researchers used various surrogate biomarkers to classify CTG, where some were clinically irrelevant. We proposed using Apgar scores as the surrogate benchmark of babies' ability to recover from birth. Apgar scores measure newborns' ability to recover from active uterine contraction, which measures appearance, pulse, grimace, activity and respiration. The higher the Apgar score, the healthier the baby is.We employ signal processing methods to pre-process and extract validated features of 552 raw CTG. We also included CTG-specific characteristics as outlined in the NICE guidelines. We employed ML techniques using 22 features and measured performances between ML classifiers. While we found that ML can distinguish CTG with low Apgar scores, results for the lowest Apgar scores, which are rare in the dataset we used, would benefit from more CTG data for better performance. We need an external dataset to validate our model for generalizability to ensure that it does not overfit a specific population.Clinical Relevance- This study demonstrated the potential of using a clinically relevant benchmark for classifying CTG to allow automatic early detection of hypoxia to reduce decision-making time in maternity units.

A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability.

BACKGROUND: Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications. METHODS: Aiming to improve CTG classification performance and prediction interpretability, a hybrid model was proposed using a stacked ensemble strategy with mixed features and Kernel SHapley Additive exPlanations (SHAP) framework. Firstly, the stacked ensemble classifier was established by employing support vector machines (SVM), extreme gradient boosting (XGB), and random forests (RF) as base learners, and backpropagation (BP) as a meta learner whose input was mixed with the CTG features and the probability value of each category output by base learners. Then, the public and private CTG datasets were used to verify the discriminative performance. Furthermore, Kernel SHAP was applied to estimate the contribution values of features and their relationships to the fetal states. RESULTS: For intelligent CTG classification using 10-fold cross-validation, the accuracy and average F1 score were 0.9539 and 0.9249 in the public dataset, respectively; and those were 0.9201 and 0.8926 in the private dataset, respectively. For interpretability, the explanation results indicated that accelerations (AC) and the percentage of time with abnormal short-term variability (ASTV) were the key determinants. Specifically, the probability of abnormality increased and that of the normal state decreased as the value of ASTV grew. In addition, the likelihood of the normal status rose with the increase of AC. CONCLUSIONS: The proposed model has high classification performance and reasonable interpretability for intelligent fetal monitoring.

Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data.

Cardiotocography (CTG) captured the fetal heart rate and the timing of uterine contractions. Throughout pregnancy, CTG intelligent categorization is crucial for monitoring fetal health and preserving proper fetal growth and development. Since CTG provides information on the fetal heartbeat and uterus contractions, which helps determine if the fetus is pathologic or not, obstetricians frequently use it to evaluate a child's physical health during pregnancy. In the past, obstetricians have artificially analyzed CTG data, which is time-consuming and inaccurate. So, developing a fetal health categorization model is crucial as it may help to speed up the diagnosis and treatment and conserve medical resources. The CTG dataset is used in this study. To diagnose the illness, 7 machine learning models are employed, as well as ensemble strategies including voting and stacking classifiers. In order to choose and extract the most significant and critical attributes from the dataset, Feature Selection (FS) techniques like ANOVA and Chi-square, as well as Feature Extraction (FE) strategies like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), are being used. We used the Synthetic Minority Oversampling Technique (SMOTE) approach to balance the dataset because it is unbalanced. In order to forecast the illness, the top 5 models are selected, and these 5 models are used in ensemble methods such as voting and stacking classifiers. The utilization of Stacking Classifiers (SC), which involve Adaboost and Random Forest (RF) as meta-classifiers for disease detection. The performance of the proposed SC with meta-classifier as RF model, which incorporates Chi-square with PCA, outperformed all other state-of-the-art models, achieving scores of 98.79%,98.88%,98.69%,96.32%, and 98.77% for accuracy, precision, recall, specificity, and f1-score respectively.

A comparative study of CTG monitoring one hour before labor in infants born with and without asphyxia.

BACKGROUND AND AIM: Asphyxia is a condition arising when the infant is deprived of oxygen, causing Fetal brain damage or death, which is associated with hypoxia and hypercapnia. Although fetal Cardiotocography (CTG) can show the Fetal health status during labor, some studies have reported cases of fetal asphyxia despite reassuring CTGs. This study hence aimed to compare FHR Monitoring and uterine contractions in the last hour before delivered between two groups of infants born with and without asphyxia. METHODOLOGY: The study was conducted on 70 pregnant women who delivered Taleghani and Al-Zahra academic teaching hospitals of Tabriz for labor in 2020-2021. RESULTS: The study data showed no significant difference between mothers of infants with and without asphyxia in terms of demographics (p > 0.05). The prevalence of asphyxia was significantly higher only in mothers with the gravidity of 3 and 4 (p = 0.003). In terms of the methods for labor induction, the use of oxytocin was more common among mothers of infants with asphyxia (74.3%) than in those of infants without asphyxia (p = 0.015). The results also revealed a significant difference between infants with and without asphyxia in the Apgar score (first, fifth, and tenth minutes), need for neonatal resuscitation, umbilical cord artery Acidosis (pH, bicarbonate, and BE), and severity of HIE between two groups of infants with asphyxia and without asphyxia (p < 0.0001). The comparison of fetal CTG 0 to 20 min before the delivery indicated that normal variability was observed in 71.4% of infants born with asphyxia, whereas this figure for infants born without asphyxia was 91.4% (p = 0.031). However, the results showed no significant difference between the two groups of infants in any of the tstudied indicators at 20 and 40 min before the labor(p > 0.05). There was a significant difference between the two groups of infants in terms of deceleration at 40 and 60 min before the labor, as it was observed in 53.6% of infants born with asphyxia and only 11.1% of those born without asphyxia. The results also demonstrated a significant difference between the two groups in the type of deceleration (p = 0.025). Pearson and Spearman correlation coefficients showed a significant and direct relationship between interpretation the CTG of the three Perinatologists(p < 0.0001, r > 0.8). CONCLUSION: The study results demonstrated a significant difference between infants born with asphyxia and those born without asphyxia in variability at 0 to 20 min before the labor and deceleration at 40 to 60 min before the labor.