Search Results (96)

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

Cardiac disease remains a leading cause of maternal morbidity and mortality, particularly in developed countries where improved survival has increased the number of pregnant patients with congenital heart disease. The physiological changes of pregnancy, such as increased blood volume, cardiac output, and hypercoagulability, can exacerbate preexisting cardiac conditions, posing significant anesthetic challenges during cesarean delivery. This review outlines anesthetic strategies for parturients with structural or functional cardiac disease, emphasizing individualized, multidisciplinary care. We examine general and regional anesthesia approaches, intraoperative monitoring, and hemodynamic goals, including fluid balance, venous return optimization, and myocardial oxygen demand reduction. Preoperative risk stratification and coordination with cardiology and obstetric teams are essential. Future efforts should aim to standardize protocols and improve maternal–fetal outcomes through evidence-based anesthetic planning. 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 case report describes a fetus diagnosed with complete atrioventricular block (CAVB) associated with positive maternal anti-Ro and anti-La antibodies, referred to our fetal cardiology unit at 25 weeks of gestation. The diagnosis of systemic lupus erythematosus (SLE) was established during the investigation of the fetal condition. Oral dexamethasone was initiated and well tolerated, with no adverse effects reported throughout the remainder of the pregnancy. The fetal heart rate (HR) remained above 50 bpm, and, therefore, no beta-sympathomimetic agents were administered. Due to progressive reduction in myocardial contractility and the appearance of early signs of endocardial fibroelastosis, intravenous immunoglobulin (IVIG) therapy was initiated. The patient was hospitalized for the infusion, which was well tolerated without complications, and a second IVIG cycle was administered four weeks later. Significant improvement in ventricular contractility and reduction in fibroelastosis were observed. As reported in the literature, no chronotropic effect was noted, and fetal HR remained stable after treatment. Weekly monitoring of cardiovascular profile score and fetal HR was maintained, with the score consistently remaining at 8 throughout gestation, supporting continued outpatient management. Delivery occurred at 36 weeks and 3 days due to spontaneous preterm labor. A male neonate weighing 3025 g was delivered with Apgar scores of 8 and 9, and an initial heart rate of 84 bpm. Neonatal electrocardiography confirmed persistent CAVB, and the newborn was monitored in the neonatal intensive care unit. At follow-up, the infant remains clinically stable and has not required permanent pacemaker implantation. Full article

Background: Cardiotocography (CTG) is widely used in obstetrics to monitor fetal heart rate and uterine contractions. It helps detect early signs of fetal distress. However, manual interpretation of CTG can be time-consuming and may vary between clinicians. Recent advances in machine learning provide more efficient and consistent alternatives for analyzing CTG data. Objectives: This study aims to investigate the classification of fetal health using various machine learning models to facilitate early detection of fetal health conditions. Methods: This study utilized a tabular dataset comprising 2126 patient records and 21 features. To classify fetal health outcomes, various machine learning algorithms were employed, including CatBoost, Decision Tree, ExtraTrees, Gradient Boosting, KNN, LightGBM, Random Forest, SVM, ANN and DNN. To address class imbalance and enhance model performance, the Synthetic Minority Oversampling Technique (SMOTE) was employed. Results: Among the tested models, the LightGBM algorithm achieved the highest performance, boasting a classification accuracy of 90.73% and, more notably, a balanced accuracy of 91.34%. This superior balanced accuracy highlights LightGBM’s effectiveness in handling imbalanced datasets, outperforming other models in ensuring fair classification across all classes. Conclusions: This study highlights the potential of machine learning models as reliable tools for fetal health classification. The findings emphasize the transformative impact of such technologies on medical diagnostics. Additionally, the use of SMOTE effectively addressed dataset imbalance, further enhancing the reliability and applicability of the proposed approach. Full article

Pre-eclampsia is a severe pregnancy complication affecting 5–8% of pregnancies worldwide, marked by high blood pressure and organ damage typically occurring after 20 weeks of gestation. It is a leading cause of maternal and fetal morbidity and mortality. Though its exact cause is unknown, it involves placental abnormalities and improper blood vessel development. Risk factors include a history of pre-eclampsia, chronic hypertension, diabetes, obesity, and autoimmune disorders. Symptoms include high blood pressure, proteinuria, headaches, vision changes, and abdominal pain. Untreated, it can lead to seizures, stroke, preterm birth, or death. Delivery is the definitive treatment, with management strategies such as monitoring and blood pressure control. Pre-eclampsia significantly increases long-term cardiovascular disease (CVD) risks, including hypertension, ischemic heart disease, and stroke, linked to shared mechanisms like endothelial dysfunction and inflammation. Women with severe or recurrent pre-eclampsia have heightened risks, often developing chronic hypertension within a decade postpartum. It also impacts offspring, with daughters at elevated risk for pre-eclampsia and CVD. Hypertensive disorders of pregnancy, including pre-eclampsia, induce changes like left ventricular hypertrophy and diastolic dysfunction, raising risks for heart failure with preserved ejection fraction and coronary atherosclerosis. Overlapping with peripartum cardiomyopathy, pre-eclampsia underscores a spectrum of pregnancy-related cardiovascular disorders. Long-term monitoring and lifestyle interventions are crucial for managing risks, with research into genetic and biological mechanisms offering the potential for targeted prevention. Full article

Fetal heart rate variability (FHRV) is a critical indicator of fetal well-being and autonomic nervous system development during labor. Traditional monitoring methods often provide limited insights, potentially leading to delayed interventions and suboptimal outcomes. This study proposes an advanced predictive analytics approach by integrating approximate entropy analysis with a hidden Markov model (HMM) within a digital twin framework to enhance real-time fetal monitoring. We utilized a dataset of 469 fetal electrocardiogram (ECG) recordings, each exceeding one hour in duration, to ensure sufficient temporal information for reliable modeling. The FHRV data were preprocessed and partitioned into parasympathetic and sympathetic components based on downward and non-downward beat detection. Approximate entropy was calculated to quantify the complexity of FHRV patterns, revealing significant correlations with umbilical cord blood gas parameters, particularly pH levels. The HMM was developed with four hidden states representing discrete pH levels and eight observed states derived from FHRV data. By employing the Baum–Welch and Viterbi algorithms for training and decoding, respectively, the model effectively captured temporal dependencies and provided early predictions of the fetal acid–base status. Experimental results demonstrated that the model achieved 85% training and 79% testing accuracy on the balanced dataset distribution, improving from 78% and 71% on the imbalanced dataset. The integration of this predictive model into a digital twin framework offers significant benefits for timely clinical interventions, potentially improving prenatal outcomes. Full article

Non-invasive fetal electrocardiography (NIFECG), an emerging technology for fetal health monitoring, has garnered significant attention in recent years. It is considered a promising alternative to traditional Doppler ultrasound methods and has the potential to become the standard approach for fetal monitoring. This paper provides a comprehensive review of the latest advancements in NIFECG technology, including signal acquisition, signal preprocessing, fetal electrocardiogram extraction, and fetal cardiac anomaly classification. Furthermore, the characteristics and limitations of existing NIFECG datasets are analyzed, and improvement suggestions are proposed. Future research directions for NIFECG technology are discussed, with a particular focus on the potential applications of deep learning techniques, multimodal data fusion, and remote monitoring systems. This review offers references and support for advancing the development and application of NIFECG monitoring technology. Full article

Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the need for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnoses, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (STIC) was developed in the early 2000s. Some of the advantages of STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed, such as fetal intelligent navigation echocardiography (FINE), also known as “5D heart”, in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to diagnose CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled the virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal–fetal interaction in cases of blind pregnant women, and interactive discussions among multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review, the authors discuss advanced image technologies that may optimize prenatal diagnoses of CHDs. Full article

Background/Objectives: Truncus arteriosus communis (TAC) is a rare congenital heart defect characterized by a single arterial trunk that supplies systemic, pulmonary, and coronary circulations. This defect, constituting approximately 1–4% of congenital heart diseases, poses significant challenges in prenatal diagnosis, management, and postnatal outcomes. Methods: A retrospective analysis was conducted at the local tertiary referral center on cases of TAC diagnosed prenatally between 2019 and 2024. Additionally, a systematic literature review was performed to evaluate the accuracy of prenatal diagnostics and the presence of associated anomalies in fetuses with TAC and compare already published data with the local results. The review included studies that especially described the use of fetal echocardiography, the course and outcome of affected pregnancies, and subsequent management strategies. Results: The analysis of local prenatal diagnoses revealed 14 cases. Of the 11 neonates who survived to birth, the TAC diagnosis was confirmed in 7 instances. With all seven neonates undergoing surgery, the intention-to-treat survival rate was 86%, and the overall survival rate was 55%. By reviewing published case series, a total of 823 TAC cases were included in the analysis, of which 576 were diagnosed prenatally and 247 postnatally. The presence of associated cardiac and extracardiac manifestations as well as genetic anomalies was common, with a 22q11 microdeletion identified in 27% of tested cases. Conclusions: Advances in prenatal imaging and early diagnosis have enhanced the management of TAC, allowing for the detailed planning of delivery and immediate postnatal care in specialized centers. The frequent association with genetic syndromes underscores the importance of genetic counseling in managing TAC. An early surgical intervention remains crucial for improving long-term outcomes, although the condition is still associated with significant risks. Long-term follow-up studies are essential to monitor potential complications and guide future management strategies. Overall, a coordinated multidisciplinary approach from prenatal diagnosis to postnatal care is essential for improving outcomes for individuals with TAC. Full article

Poor placental development and placental defects can lead to adverse pregnancy outcomes such as pre-eclampsia, fetal growth restriction, and stillbirth. This study introduces two sensors, which use a near-infrared spectroscopy (NIRS) technique to measure placental oxygen saturation transabdominally. The first one, an NIRS sensor, is a wearable device consisting of multiple NIRS channels. The second one, a Multimodal sensor, which is an upgraded version of the NIRS sensor, is a wireless and wearable device, integrating a motion sensor and multiple NIRS channels. A pilot clinical study was conducted to assess the feasibility of the two sensors in measuring transabdominal placental oxygenation in 36 pregnant women (n = 12 for the NIRS sensor and n = 24 for the Multimodal sensor). Among these subjects, 4 participants had an uncomplicated pregnancy, and 32 patients had either maternal pre-existing conditions/complications, neonatal complications, and/or placental pathologic abnormalities. The study results indicate that the patients with maternal complicated conditions (69.5 ± 5.4%), placental pathologic abnormalities (69.4 ± 4.9%), and neonatal complications (68.0 ± 5.1%) had statistically significantly lower transabdominal placental oxygenation levels than those with an uncomplicated pregnancy (76.0 ± 4.4%) (F (3,104) = 6.6, p = 0.0004). Additionally, this study shows the capability of the Multimodal sensor in detecting the maternal heart rate and respiratory rate, fetal movements, and uterine contractions. These findings demonstrate the feasibility of the two sensors in the real-time continuous monitoring of transabdominal placental oxygenation to detect at-risk pregnancies and guide timely clinical interventions, thereby improving pregnancy outcomes. Full article

Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus’s health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time–frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies—feature-level fusion, decision-level fusion, and model-level fusion—achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85% and sensitivity of 97%. Full article

Introduction: The prevalence of ductus venosus agenesis (ADV) in singleton pregnancies ranges from 0.04% to 0.15%, while its prevalence in twins remains largely unknown. To our knowledge, in the literature, there is only a single case report of a monochorionic diamniotic (MCDA) pregnancy complicated by ADV. Fetuses with ADV are at increased risk for congenital cardiac disease, heart failure, and fetal growth restriction (FGR). Consequently, these pregnancies have a heightened risk of experiencing an adverse outcome, like stillbirth and neonatal or infant death. Closer antenatal monitoring is warranted when ADV is suspected. Currently, there are no guidelines regarding the standard of care in cases of ADV and no recommendations for the timing of delivery in either singleton or twin pregnancies. Cases: This study aims to provide a comprehensive overview of the management of twin pregnancies complicated by ADV, featuring two cases of MC twins with concurrent sFGR and ADV in one twin. Discussion: These pregnancies experienced completely different outcomes, underscoring the necessity for personalized management tailored to the specific risk factors present in each pregnancy. Typically, in MCDA pregnancies with severe sFGR (type II and III), delivery represents the most reasonable option when venous Doppler abnormalities are identified. However, the absence of the DV complicates the management and the process of decision-making regarding the timing of delivery in cases of sFGR and ADV. We emphasize that effective decision-making should be guided by the presence of additional risk factors, including velamentous insertion, significant estimated fetal weight discordance, and progressive deterioration of the Doppler over time. Conclusions: Our experience suggests that these factors are strongly correlated with poorer outcomes. Given this context, could it be acceptable, in the case of MC pregnancy complicated by severe sFGR and ADV, with worsening findings and additional risk factors (e.g., velamentous insertion, severe birth weight discrepancy), to anticipate the time of delivery starting from 30 weeks of gestational age? Full article

This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern and traditional monitoring techniques, such as electrocardiography (ECG), ballistocardiography (BCG), phonocardiography (PCG), and cardiotocography (CTG), in a variety of obstetric scenarios. A particular focus is on the most recent developments in textile-based wearables for fHR monitoring. These innovative devices mark a substantial advancement in the field and are noteworthy for their continuous data collection capability and ergonomic design. The review delves into the obstacles that arise when incorporating these wearables into clinical practice. These challenges include problems with signal quality, user compliance, and data interpretation. Additionally, it looks at how these technologies could improve fetal health surveillance by providing expectant mothers with more individualized and non-intrusive options, which could change the prenatal monitoring landscape. Full article

Clinical fetal monitoring devices can only be operated by medical professionals and are overly costly, prone to detrimental false positives, and emit radiation. Thus, highly accurate, easily accessible, simplified, and cost-effective fetal monitoring devices have gained an enormous interest in obstetrics. In this study, a cost-effective and user-friendly wearable home fetal movement and distress detection device is developed and assessed for early-stage design progression by facilitating continuous, comfortable, and non-invasive monitoring of the fetus during the final trimester. The functionality of the developed prototype is mainly based on a microcontroller, a single accelerometer, and a specialized fetal phonocardiography (fPCG) acquisition board with a low-cost microphone. The developed system is capable of identifying fetal movement and monitors fetal heart rhythm owing to its considerable sensitivity. Further, the device includes a Global System for Mobile Communication (GSM)-based alert system for instant distress notifications to the mother, proxy, and emergency services. By incorporating digital signal processing, the system achieves zero false negatives in detecting fetal movements, which was validated against an open-source database. The acquired results clearly substantiated the efficacy of the fPCG acquisition board and alarm system, ensuring the prompt identification of fetal distress. Full article