AI Medical Compendium Topic

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Live Birth

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Deep learning neural network analysis of human blastocyst expansion from time-lapse image files.

Reproductive biomedicine online
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...

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
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...

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

Briefings in bioinformatics
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)...

Artificial intelligence in the embryology laboratory: a review.

Reproductive biomedicine online
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...

Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

BMC pregnancy and childbirth
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 ...

An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos.

Artificial intelligence in medicine
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...

Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.

Reproductive biomedicine online
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?

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

Human reproduction (Oxford, England)
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?