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Labor, Obstetric

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Changes in maternal placental growth factor levels during term labour.

Placenta
UNLABELLED: Placental growth factor (PlGF) has important angiogenic function that is critical to placental development. Lower levels of PlGF are associated with fetal growth restriction, pre-eclampsia and intrapartum fetal compromise. The aim of this...

The ResQu Index: A new instrument to appraise the quality of research on birth place.

PloS one
OBJECTIVE: Place of birth is a known determinant of health care outcomes, interventions and costs. Many studies have examined the maternal and perinatal outcomes when women plan to give birth in hospitals compared with births in birth centres or at h...

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces.

Computers in biology and medicine
Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and int...

Deep Learning for Continuous Electronic Fetal Monitoring in Labor.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Continuous electronic fetal monitoring (EFM) is used worldwide to visually assess whether a fetus is exhibiting signs of distress during labor, and may benefit from an emergency operative delivery (e.g. Cesarean section). Previously, computerized EFM...

Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.

Scientific reports
Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Wome...

Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.

American journal of perinatology
OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings.

Biosensors
We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analy...

Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study.

BMC medical informatics and decision making
BACKGROUND: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result f...

Threats by artificial intelligence to human health and human existence.

BMJ global health
While artificial intelligence (AI) offers promising solutions in healthcare, it also poses a number of threats to human health and well-being via social, political, economic and security-related determinants of health. We describe three such main way...

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.

Scientific reports
Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and...