AIMC Topic: Live Birth
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Assessment of artificial intelligence model and manual morphokinetic annotation system as embryo grading methods for successful live birth prediction: a retrospective monocentric study.
Reproductive biology and endocrinology : RB&E
Mar 5, 2024
PURPOSE: The introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at investigating the efficiency of KIDS...
Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.
Reproductive biomedicine online
Oct 2, 2023
RESEARCH QUESTION: Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?
An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos.
Artificial intelligence in medicine
Nov 2, 2022
In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical AR...
Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study.
Journal of assisted reproduction and genetics
Jul 26, 2022
PROPOSE: Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes?
Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.
BMC pregnancy and childbirth
Jan 16, 2022
BACKGROUND: Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the ...
Artificial intelligence in the embryology laboratory: a review.
Reproductive biomedicine online
Nov 12, 2021
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecti...
Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth.
Reproductive biomedicine online
May 15, 2021
RESEARCH QUESTION: Can artificial intelligence (AI) improve the prediction of live births based on embryo images?
Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study.
BMC pregnancy and childbirth
Mar 12, 2021
BACKGROUND: Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the f...
Deep learning neural network analysis of human blastocyst expansion from time-lapse image files.
Reproductive biomedicine online
Mar 6, 2021
RESEARCH QUESTION: Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts fo...
Machine learning predicts live-birth occurrence before in-vitro fertilization treatment.
Scientific reports
Dec 1, 2020
In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to ...
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