AIMC Topic: Fertilization in Vitro

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Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

Reproductive biomedicine online
RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian ...

Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle.

Reproductive biomedicine online
RESEARCH QUESTION: Which machine learning model predicts the implantation outcome better in an IVF cycle? What is the importance of each variable in predicting the implantation outcome in an IVF cycle?

Stain-free detection of embryo polarization using deep learning.

Scientific reports
Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, im...

The Comparison of Fixed and Flexible Progestin Primed Ovarian Stimulation on Mature Oocyte Yield in Women at Risk of Premature Ovarian Insufficiency.

Frontiers in endocrinology
While gonadotrophin releasing hormone (GnRH) antagonists have been the standard of pituitary suppression during ovarian stimulation for ART, progestin primed ovarian stimulation (PPOS) has emerged as an alternative. Progestins can be started simultan...

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.

PloS one
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep le...

Candidate Circulating Biomarkers of Spontaneous Miscarriage After IVF-ET Identified via Coupling Machine Learning and Serum Lipidomics Profiling.

Reproductive sciences (Thousand Oaks, Calif.)
Spontaneous miscarriage is a common pregnancy complication. Multiple etiologies have been proposed such as genetic aberrations, endocrinology disorder, and immunologic derangement; however, the relevance of circulating lipidomes to the specific condi...

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...

An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.

Reproductive biology and endocrinology : RB&E
BACKGROUND: For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo p...

Artificial intelligence in the embryology laboratory: a review.

Reproductive biomedicine online
The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecti...

An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions.

Reproductive biomedicine online
RESEARCH QUESTION: Can workflow during IVF be facilitated by artificial intelligence to limit monitoring during ovarian stimulation to a single day and enable level-loading of retrievals?