AIMC Topic: Blastocyst

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Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.

Lab on a chip
Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly sub...

Automatic grading of human blastocysts from time-lapse imaging.

Computers in biology and medicine
BACKGROUND: Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges,...

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018.

Journal of assisted reproduction and genetics
Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every as...

Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens.

Sensors (Basel, Switzerland)
In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers ...

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Journal of assisted reproduction and genetics
Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rate...

A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.

Scientific reports
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method ...

Computer vision for automatic identification of blastocyst structures and blastocyst formation time in In-Vitro Fertilization.

Computers in biology and medicine
Embryo selection is an indispensable step to ensure the success of In-Vitro Fertilization; however, this decision is a time-consuming, laborious, and highly subjective task for embryologists. In the best scenarios, when implanting an embryo of the be...

An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.

Human reproduction (Oxford, England)
STUDY QUESTION: Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth?

Deep learning classification integrating embryo images with associated clinical information from ART cycles.

Scientific reports
An advanced Artificial Intelligence (AI) model that leverages cutting-edge computer vision techniques to analyse embryo images and clinical data, enabling accurate prediction of clinical pregnancy outcomes in single embryo transfer procedures. Three ...

Artificial intelligence-driven analysis of embryo morphokinetics in singleton, twin, and triplet pregnancies.

Human reproduction (Oxford, England)
In recent years, the transfer of more than one embryo has become less frequent to diminish multiple pregnancies. Even so, there is still a risk of one embryo splitting into two or even three. This report presents the case of a triamniotic monochorion...