AIMC Topic: Blastocyst
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Development and validation of deep learning based embryo selection across multiple days of transfer.
Scientific reports
Mar 14, 2023
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dat...
Making and selecting the best embryo in the laboratory.
Fertility and sterility
Dec 13, 2022
Over the past 4 decades our ability to maintain a viable human embryo in vitro has improved dramatically, leading to higher implantation rates. This has led to a notable shift to single blastocyst transfer and the ensuing elimination of high order mu...
Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI).
Journal of assisted reproduction and genetics
Nov 14, 2022
PURPOSE: Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in th...
Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?
Reproductive biomedicine online
Oct 1, 2022
RESEARCH QUESTION: Does embryo categorization by existing artificial intelligence (AI), morphokinetic or morphological embryo selection models correlate with blastocyst euploidy?
Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos.
Reproductive biomedicine online
Aug 13, 2022
RESEARCH QUESTION: What is the association between the deep learning-based scoring system, iDAScore, and biological events during the pre-implantation period?
Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.
Journal of assisted reproduction and genetics
Aug 13, 2022
PURPOSE: To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.
An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos.
Reproductive biomedicine online
Aug 3, 2022
RESEARCH QUESTION: Can better methods be developed to evaluate the performance and characteristics of an artificial intelligence model for evaluating the likelihood of clinical pregnancy based on analysis of day-5 blastocyst-stage embryos, such that ...
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?
Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?
Reproductive biomedicine online
Jun 17, 2022
RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data i...
Does conventional morphological evaluation still play a role in predicting blastocyst formation?
Reproductive biology and endocrinology : RB&E
Apr 19, 2022
BACKGROUND: Advanced models including time-lapse imaging and artificial intelligence technologies have been used to predict blastocyst formation. However, the conventional morphological evaluation of embryos is still widely used. The purpose of the p...
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