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Pregnancy Outcome

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[Updated application of artificial intelligence in reproductive medicine].

Zhonghua nan ke xue = National journal of andrology
With the rapid development of precision medicine and big data application, artificial intelligence (AI) has become a frontier technology in medical research. AI can be applied to the clinical diagnosis and treatment of reproductive diseases, predicti...

Performance of a deep learning based neural network in the selection of human blastocysts for implantation.

eLife
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistiv...

Application of artificial intelligence in screening for adverse perinatal outcomes: A protocol for systematic review.

Medicine
The article presents a systematic review protocol. The aim of the study is an assessment of current studies regarding the application of artificial intelligence and neural networks in the screening for adverse perinatal outcomes. We intend to compare...

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.

Frontiers in immunology
Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely a...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

Archives of gynecology and obstetrics
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.

A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Fertility and sterility
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

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
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...

Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET).

Taiwanese journal of obstetrics & gynecology
OBJECTIVE: To decrease multiple pregnancy risk and sustain optimal pregnancy chance by choosing suitable number of embryos during transfer, this study aims to construct artificial intelligence models to predict the pregnancy outcome and multiple preg...