AIMC Topic: Embryo Culture Techniques

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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.

Scientific reports
Predicting blastocyst formation poses significant challenges in reproductive medicine and critically influences clinical decision-making regarding extended embryo culture. While previous research has primarily focused on determining whether an IVF cy...

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...

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...

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...

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?

Does conventional morphological evaluation still play a role in predicting blastocyst formation?

Reproductive biology and endocrinology : RB&E
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...

Time-Lapse Systems: A Comprehensive Analysis on Effectiveness.

Seminars in reproductive medicine
Time-lapse systems have quickly become a common feature of in vitro fertilization laboratories all over the world. Since being introduced over a decade ago, the alleged benefits of time-lapse technology have continued to grow, from undisturbed cultur...