AIMC Topic: Live Birth
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An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study.
Reproductive biomedicine online
Oct 8, 2020
RESEARCH QUESTION: The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology...
Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service.
JBRA assisted reproduction
Oct 6, 2020
Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obsta...
Machine learning vs. classic statistics for the prediction of IVF outcomes.
Journal of assisted reproduction and genetics
Aug 11, 2020
PURPOSE: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.
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...
Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method.
Journal of translational medicine
Sep 23, 2019
BACKGROUND: Infertility has become a global health issue with the number of couples seeking in vitro fertilization (IVF) worldwide continuing to rise. Some couples remain childless after several IVF cycles. Women undergoing IVF face greater risks and...
An artificial neural network for the prediction of assisted reproduction outcome.
Journal of assisted reproduction and genetics
Jun 19, 2019
PURPOSE: To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.
An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.
Human reproduction (Oxford, England)
Jun 1, 2025
STUDY QUESTION: Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth?
BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.
Human reproduction (Oxford, England)
Apr 3, 2024
STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.
Briefings in bioinformatics
Sep 2, 2021
OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML)...
High Peak Estradiol Predicts Higher Miscarriage and Lower Live Birth Rates in High Responders Triggered with a GnRH Agonist in IVF/ICSI Cycles.
The Journal of reproductive medicine
Jan 1, 2015
OBJECTIVE: To investigate parameters predictive of pregnancy outcomes in high responders undergoing fresh, autologous, GnRH antagonist IVF/ICSI cycles using a GnRH agonist trigger.
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