AIMC Topic: Blastocyst

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Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study.

Reproductive biomedicine online
RESEARCH QUESTION: What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?

Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning.

Journal of assisted reproduction and genetics
PURPOSE: This article aims to assess how differences in maternal age distributions between IVF clinics affect the performance of an artificial intelligence model for embryo viability prediction and proposes a method to account for such differences.

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction.

Fertility and sterility
This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic testing in in vitro fertilization procedures. The current gold standard, preimplantation genetic te...

How should the best human embryo in vitro be? Current and future challenges for embryo selection.

Minerva obstetrics and gynecology
In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cy...

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.

Scientific data
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potent...

Development and validation of deep learning based embryo selection across multiple days of transfer.

Scientific reports
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
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
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
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
RESEARCH QUESTION: What is the association between the deep learning-based scoring system, iDAScore, and biological events during the pre-implantation period?