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Pregnancy

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Exploring the potential of machine learning in gynecological care: a review.

Archives of gynecology and obstetrics
Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecolog...

Deep neural network (DNN) modelling for prediction of the mode of delivery.

European journal of obstetrics, gynecology, and reproductive biology
One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tool...

The association between PM components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

Environmental research
BACKGROUND: PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develo...

Machine learning model based on RCA-PDCA nursing methods and differentiating factors to predict hypotension during cesarean section surgery.

Computers in biology and medicine
BACKGROUND: Intraoperative hypotension during cesarean section has become a serious complication for maternal and fetal healthy. It is commonly encountered by subarachnoid anesthesia. However, currently used control methods have varying degrees of si...

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.

Biomedical engineering online
BACKGROUND: Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary...

Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey.

Current opinion in obstetrics & gynecology
PURPOSE OF REVIEW: This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolut...

Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy.

Prenatal diagnosis
OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmen...

Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.

Nature communications
Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the 'Histology Analysi...

Prediction of metabolic syndrome following a first pregnancy.

American journal of obstetrics and gynecology
BACKGROUND: The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome.