Search Results (13)

Background/Objectives: Electronic fetal monitoring (EFM) has been used for intrapartum fetal surveillance for over 50 years. Despite numerous trials comparing EFM with standard fetal heart rate (FHR) auscultation, it remains contentious whether continuous monitoring with standard interpretation has reliably improved perinatal outcomes, specifically lower rates of perinatal morbidity and mortality. This review examines previous attempts to improve fetal monitoring and presents future directions for novel intrapartum fetal surveillance systems. Methods: We conducted a chronological review of EFM developments, including ancillary methods such as fetal ECG analysis, automated systems for FHR analysis, and artificial intelligence applications. We analyzed the evolution from visual interpretation to intelligent systems and evaluated the performance of various automated monitoring platforms. Results: Various ancillary methods developed to improve EFM accuracy for predicting fetal compromise have shown limited success. Only a limited number of studies demonstrated that adding fetal ECG analysis to visual FHR pattern interpretation resulted in better fetal outcomes. Automated systems for FHR analysis have not consistently enhanced intrapartum fetal surveillance. However, novel approaches such as the Fetal Reserve Index (FRI) show promise by incorporating clinical risk factors with traditional FHR patterns to provide higher-level risk assessment and prognosis. Conclusions: The shortcomings of visual interpretation of FHR patterns persist despite technological advances. Future intelligent intrapartum surveillance systems must combine conventional fetal monitoring with comprehensive risk assessment that incorporates maternal, fetal, and obstetric factors. The integration of artificial intelligence with contextualized metrics like the FRI represents the most promising direction for improving intrapartum fetal surveillance and clinical outcomes. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background and Objectives: Electronic fetal heart rate monitoring is mandatory for preterm labor. Moderate to late preterm neonates have an increased risk of overall morbidity, neonatal intensive care (NICU) admission, and consequently, medication use. The outcome of preterm neonates > 32 weeks of gestation in relation to three-tiered fetal heart rate (FHR) categorization was analyzed. Materials and Methods: This was a single-center, retrospective case-control study conducted from January 2021 to December 2023. The study included 25 FGR and 131 control cases born from 33 to 36 6/7 gestational weeks. Outcome was defined as the need for assistance after birth in first 15 min of life, respiratory outcome, and first day dopamine use and fresh frozen plasma transfusion. Maternal characteristics as risk factors for non-normal categories within three-tiered FHR categorization were also analyzed. Results: There was no significant difference in neonatal outcome among groups, except significantly lower 1 min APGAR and longer LOS in the FGR group. An increasing category within the three-tiered FHR categorization positively correlated with the need for assistance after birth, respiratory outcome, dopamine use, fresh frozen plasma transfusion, and length of hospital stay. Negative correlations were revealed between the increasing category within the three-tiered FHR categorization and first and fifth minute APGAR scores. Oligohydramnios and male sex were risk factors for non-normal categories within three-tiered FHR categorization. The correlation was tested using the Spearman correlation coefficient. A logistic regression model was employed to identify maternal risk factors for the non-normal category within three-tiered FHR categorization. All differences were statistically significant (p < 0.05). Conclusions: The increasing category within three-tiered FHR categorization may alert neonatologists to be highly suspicious of RDS, respiratory support, dopamine use, and fresh frozen plasma transfusion in neonates born from 33 to 36 6/7 gestational weeks. Oligohydramnios and male sex increase the probability for non-normal categories in the three-tiered FHR categorization. Full article

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus’s condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy. Full article

Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections. Full article

We aimed to investigate the correlation between total deceleration area (TDA), neonatal birthweight and neonatal acidemia in vacuum extractions (VEs). This is a retrospective study in a tertiary hospital, including VE performed due to non-reassuring fetal heart rate (NRFHR). Electronic fetal monitoring during the 120 min preceding delivery was interpreted by two obstetricians who were blinded to neonatal outcomes. TDA was calculated as the sum of the area under the curve for each deceleration. Neonatal birthweights were classified as low (<2500 g), normal (2500–3999 g) or macrosomic (>4000 g). A total of 85 VEs were analyzed. Multivariable linear regression, adjusted for gestational age, nulliparity and diabetes mellitus, revealed a negative correlation between TDA in the 60 min preceding delivery and umbilical cord pH. For every 10 K increase in TDA, the cord pH decreased by 0.02 (p = 0.038; 95%CI, −0.05–0.00). The use of the Ventouse-Mityvac cup was associated with a 0.08 decrease in cord pH as compared to the Kiwi OmniCup (95%CI, −0.16–0.00; p = 0.049). Low birthweights, compared to normal birthweights, were not associated with a change in cord pH. To conclude, a significant correlation was found between TDA during the 60 min preceding delivery and cord pH in VE performed due to NRFHR. Full article

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections. Full article

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers. Full article

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors. Full article

Background and Objectives: Prematurity is currently a serious public health issue worldwide, because of its high associated morbidity and mortality. Optimizing the management of these pregnancies is of high priority to improve perinatal outcomes. One tool frequently used to determine the degree of fetal wellbeing is cardiotocography (CTG). A review of the available literature on fetal heart rate (FHR) monitoring in preterm fetuses shows that studies are scarce, and the evidence thus far is unclear. The lack of reference standards for CTG patterns in preterm fetuses can lead to misinterpretation of the changes observed in electronic fetal monitoring (EFM). The aims of this narrative review were to summarize the most relevant concepts in the field of CTG interpretation in preterm fetuses, and to provide a practical approach that can be useful in clinical practice. Materials and Methods: A MEDLINE search was carried out, and the published articles thus identified were reviewed. Results: Compared to term fetuses, preterm fetuses have a slightly higher baseline FHR. Heart rate is faster in more immature fetuses, and variability is lower and increases in more mature fetuses. Transitory, low-amplitude decelerations are more frequent during the second trimester. Transitory increases in FHR are less frequent and become more frequent and increase in amplitude as gestational age increases. Conclusions: The main characteristics of FHR tracings changes as gestation proceeds, and it is of fundamental importance to be aware of these changes in order to correctly interpret CTG patterns in preterm fetuses. Full article

In an effort to reduce newborn mortality, a newly developed strap-on electronic fetal heart rate monitor was introduced at several health facilities in Tanzania in 2015. Training sessions were organized to teach staff how to use the device in clinical settings. This study explores skilled birth attendants’ perceptions and experiences acquiring and transferring knowledge about the use of the monitor, also called Moyo. Knowledge about this learning process is crucial to further improve training programs and ensure correct, long-term use. Five Focus group discussions (FGDs) were carried out with doctors and nurse-midwives, who were using the monitor in the labor ward at two health facilities in Tanzania. The FGDs were analyzed using qualitative content analysis. The study revealed that the participants experienced the training about the device as useful but inadequate. Due to high turnover, a frequently mentioned challenge was that many of the birth attendants who were responsible for training others, were no longer working in the labor ward. Many participants expressed a need for refresher trainings, more practical exercises and more theory on labor management. The study highlights the need for frequent trainings sessions over time with focus on increasing overall knowledge in labor management to ensure correct use of the monitor over time. Full article

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Full article

To increase labor monitoring and prevent neonatal morbidity and mortality, a new wireless, strap-on electronic fetal heart rate monitor called Moyo was introduced in Tanzania in 2016. As part of the ongoing evaluation of the introduction of the monitor, the aim of this study was to explore the attitudes and perceptions of women who had worn the monitor continuously during their most recent delivery and perceptions about how it affected care. This knowledge is important to identify barriers towards adaptation in order to introduce new technology more effectively. We carried out 20 semi-structured individual interviews post-labor at two hospitals in Tanzania. A thematic content analysis was used to analyze the data. Our results indicated that the use of the monitor positively affected the women’s birth experience. It provided much-needed reassurance about the wellbeing of the child. The women considered that wearing Moyo improved care due to an increase in communication and attention from birth attendants. However, the women did not fully understand the purpose and function of the device and overestimated its capabilities. This highlights the need to improve how and when information is conveyed to women in labor. Full article