Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

The Correlation between Fetal Monitor Decelerations Area and Neonatal Cord pH among Preeclamptic parturient: an Automated Approach to a Clinical Challenge

OBJECTIVE

 To examine whether a correlation exists between electronic fetal monitoring (EFM) in the final 120 minute prior to delivery and neonatal cord blood pH among parturients with preeclampsia.

STUDY DESIGN

 A retrospective cohort study of parturients with term and near-term preeclampsia who delivered at Soroka University Medical Center between 2013 and 2020 was conducted. EFM tracings, demographical data, and obstetrical outcomes were retrieved from the patients' electronic medical records. Using MATLAB program, area under the curve (AUC) measurements were applied. Correlation was calculated using the Spearman's correlation coefficient.

RESULTS

 EFM of 88 parturients with preeclampsia were assessed. A significant negative correlation was demonstrated between cord blood pH and the decelerations AUC, total decelerations duration, and deceleration depth. No correlation was demonstrated between area over the curve (acceleration) and cord blood pH. In a multivariate linear regression, total deceleration AUC was found independently negatively associated with cord blood pH (beta = -0.264, p = 0.011) controlling for second stage of labor duration and induction of labor.

CONCLUSION

 Among parturients with preeclampsia, EFM indices may assist in the diagnosis of fetal acidemia. Further studies are needed to strengthen and establish the use of electronic EFM in the management of labor and delivery.

KEY POINTS

· A negative correlation was demonstrated between cord blood pH and the decelerations AUC.. · Total deceleration AUC was found independently negatively associated.. · Among parturients with preeclampsia, EFM indices may assist in the diagnosis of fetal acidemia..

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.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

A second look at intrapartum fetal surveillance and future directions

Intrapartum fetal surveillance aims to predict significant fetal hypoxia and institute timely intervention to avoid fetal injury, and do so without unnecessary operative delivery of fetuses at no risk of intrapartum hypoxia. However, the configuration and application of current clinical guidelines inadvertently undermine these aims because of persistent failure to incorporate increased understanding of fetal cardiovascular physiology and adaptations to oxygen deprivation, advances in signal acquisition/processing, and related technologies. Consequently, the field on intrapartum fetal surveillance is stuck in rudimentary counts of the fetal R-R intervals and visual assessment of very common, but nonspecific fetal heart decelerations and fetal heart rate variability. The present authors argue that the time has come to move away from classifications of static morphological appearances of FHR decelerations, which do not assist the thinking clinician in understanding how the fetus defends itself and compensates for intrapartum hypoxic ischaemic insults or the patterns that suggest progressive loss of compensation. We also reappraise some of the controversial aspects of intrapartum fetal surveillance in modern obstetric practice, the current state of flux in training and certification, and contemplate the future of the field particularly in the context of the emerging role of artificial intelligence.