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Embryo Culture Techniques

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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?

Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?

Human reproduction (Oxford, England)
STUDY QUESTION: What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm?

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...

Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?

Frontiers in endocrinology
PURPOSE: With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in ...

A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.

Journal of ovarian research
BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to...

[Application of the blastomere count variations "skip value" in the embryo AI assessment].

Zhonghua fu chan ke za zhi
To explore the correlation between blastomere count variations "skip value" which extracted from by time-lapse technology (TLT) combined with artificial intelligence (AI) and morphological features of in vitro fertilization (IVF) embryo, and to test...

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

Scientific reports
The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the l...

A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.

Scientific reports
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer o...

Time will tell: time-lapse technology and artificial intelligence to set time cut-offs indicating embryo incompetence.

Human reproduction (Oxford, England)
STUDY QUESTION: Can more reliable time cut-offs of embryo developmental incompetence be generated by combining time-lapse technology (TLT), artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A)?