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

Esteban-Escaño, Javier; Castán, Berta; Castán, Sergio; Chóliz-Ezquerro, Marta; Asensio, César; Laliena, Antonio R.; Sanz-Enguita, Gerardo; Sanz, Gerardo; Esteban, Luis; Savirón-Cornudella, Ricardo

doi:10.3390/e24010068

Cited by 9 publications

(7 citation statements)

References 46 publications

(54 reference statements)

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“…From the experiment results, they concluded that machine learning models perform better as compared to traditional ones. The RF and NN produced quite efficient results with AUC values of 0.865 and 0.857 for validation data (confidence interval: CI 95%) as compared to the LRM (AUC value = 0.840 and CI = 95%) [199].…”

Section: Classification and Performance Evaluationmentioning

confidence: 97%

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

Hussain,

O’Halloran,

McDermott

et al. 2024

Biomed. Phys. Eng. Express

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

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Section: Classification and Performance Evaluationmentioning

confidence: 97%

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

Hussain,

O’Halloran,

McDermott

et al. 2024

Biomed. Phys. Eng. Express

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“…Unlike previous studies that relied on manual methods of EFM record measurements, which are time-consuming and less practical in a clinical setting, our study utilized a computerized method, enabling us to move towards realtime analysis of fetal heart rate. The exploration of automated EFM analysis has also gained attention in recent research, with studies investigating real-time algorithmic analysis [28,29].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…A study conducted in 2021 [28] compared different models and highlighted the importance of AUC in predicting fetal acidemia. While machine learning methods employed in these studies are effective at identifying relationships that may be hidden from human reasoning, they often lack traceability and intuitive interpretation [30].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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

Rotem,

Theodor,

Hirsch

et al. 2023

Am J Perinatol

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Objective: To examine whether a correlation exists between electronic fetal heart monitoring (EFM) in the final 120 min prior to delivery and neonatal cord blood pH among parturients with preeclampsia. Methods: A retrospective cohort study of parturients with term and near-term preeclampsia who delivered at soroka university medical center between 2013-2020 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 Spearman 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. Conclusions: 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

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“…Machine learning algorithms possess the ability to handle vast amounts of data derived from the FHR signal and identify patterns that may not be easily discernible through traditional methods, enabling the analysis of complex relationships [15]. This is particularly crucial in predicting acidemia, as subtle changes in the FHR signal can indicate the onset of fetal distress [16,17].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia

et al. 2023

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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.

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

Cited by 9 publications

References 46 publications

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

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

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

Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia

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