AIMC Topic: Pregnancy Complications
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Deep Learning Algorithm-Based Magnetic Resonance Imaging Feature-Guided Serum Bile Acid Profile and Perinatal Outcomes in Intrahepatic Cholestasis of Pregnancy.
Computational and mathematical methods in medicine
Jun 6, 2022
This study was aimed to explore magnetic resonance imaging (MRI) based on deep learning belief network model in evaluating serum bile acid profile and adverse perinatal outcomes of intrahepatic cholestasis of pregnancy (ICP) patients. Fifty ICP pregn...
[The role of bile acid measurement in the management of intrahepatic cholestasis of pregnancy].
Orvosi hetilap
May 15, 2022
Introduction: Intrahepatic cholestasis of pregnancy complicates 1% of pregnancies. It increases the risk of severe fetal complications significantly, including preterm delivery and stillbirth. Objective: To summarize our experience with serum total b...
Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.
Scientific reports
Dec 17, 2021
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d...
Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy.
Computational and mathematical methods in medicine
Oct 29, 2021
OBJECTIVE: To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube pl...
Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.
Scientific reports
Sep 7, 2021
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed model...
Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.
Archives of gynecology and obstetrics
Feb 19, 2021
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements.
Artificial intelligence: The current state of affairs for AI in pregnancy and labour.
Journal of gynecology obstetrics and human reproduction
Dec 31, 2020
Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.
Journal of the American Heart Association
Jul 14, 2020
Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women...
Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.
Journal of pharmacokinetics and pharmacodynamics
Apr 11, 2020
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.
Scientific reports
Mar 24, 2020
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...
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