Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.

Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.

Stillbirth is a devastating pregnancy complication that still affects many women, particularly from low and middle-income countries. It is often labeled as "unexplained" and therefore unpreventable, despite the knowledge that placental dysfunction has been identified as a leading cause of antepartum stillbirth. Currently, screening for pregnancies at high-risk for placental dysfunction relies on checklists of maternal risk factors and serial measurement of symphyseal-fundal height to identify small for gestational age fetuses. More recently, the first-trimester combined screening algorithm developed by the Fetal Medicine Foundation has emerged as a better tool to predict and prevent early-onset placental dysfunction and its main outcomes of preterm preeclampsia, fetal growth restriction and stillbirth by the appropriate use of Aspirin therapy, serial growth scans and induction of labour from 40 weeks for women identified at high-risk by such screening. There is currently no equivalent to predict and prevent late-onset placental dysfunction, although algorithms combining an ultrasound-based estimation of fetal weight, assessment of maternal and fetal Doppler indices, and maternal serum biomarkers show promise as emerging new screening tools to optimize pregnancy monitoring and timing of delivery to prevent stillbirth. In this review we discuss the strategies to predict and prevent stillbirths based on first-trimester screening as well as fetal growth and wellbeing assessment in the second and third trimesters.

Stillbirth is a devastating pregnancy complication that still affects many women, particularly from low and middle-income countries. It is often labeled as "unexplained" and therefore unpreventable, despite the knowledge that placental dysfunction has been identified as a leading cause of antepartum stillbirth. Currently, screening for pregnancies at high-risk for placental dysfunction relies on checklists of maternal risk factors and serial measurement of symphyseal-fundal height to identify small for gestational age fetuses. More recently, the first-trimester combined screening algorithm developed by the Fetal Medicine Foundation has emerged as a better tool to predict and prevent early-onset placental dysfunction and its main outcomes of preterm preeclampsia, fetal growth restriction and stillbirth by the appropriate use of Aspirin therapy, serial growth scans and induction of labour from 40 weeks for women identified at high-risk by such screening. There is currently no equivalent to predict and prevent late-onset placental dysfunction, although algorithms combining an ultrasound-based estimation of fetal weight, assessment of maternal and fetal Doppler indices, and maternal serum biomarkers show promise as emerging new screening tools to optimize pregnancy monitoring and timing of delivery to prevent stillbirth. In this review we discuss the strategies to predict and prevent stillbirths based on first-trimester screening as well as fetal growth and wellbeing assessment in the second and third trimesters.