What are the types and frequency of errors occurring in the laboratory identified by an electronic witnessing system (EWS)?

Does embryo stage, hatching versus hatched blastocyst, affect ploidy status and implantation rates (IR)?

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training.

DoB is a significant biomarker of embryo success. Subgroups that examine DoE versus DoB show a significant difference when looking at hatching blastocysts, but not the earlier expansion stages. This could be due to the thinning zona in combination with the degree of growth in hatching blastocysts, but further research is required to elaborate on the difference.

In human embryos, the initiation of transcription (embryonic genome activation [EGA]) occurs by the eight-cell stage, but its exact timing and profile are unclear. To address this, we profiled gene expression at depth in human metaphase II oocytes and bipronuclear (2PN) one-cell embryos.

This study presents a series of analyses to characterize the potential benefits and limitations of using an artificial intelligence model for ranking blastocyst stage embryos.

ICSI-derived 0PN and 1PN zygotes may not be failed/abnormally fertilized oocytes. However, their blastocysts should undergo additional genetic testing, particularly 1PN female blastocysts, in which confirmation of biparental chromosome inheritance is recommended to reduce unwanted pregnancy losses.

Methods for selecting the first blastocyst to thaw and transfer are not yet well developed. Further research is needed to optimize such selection, in order to reduce the number of transfers needed to achieve pregnancy.

The objective measures of performance can lead to improved system efficiency, cost reduction, ease of incorporation of new procedures or services, realistic expectations of new hire training, and defining of reasonable targets for growth.

As the use of conventional insemination increases, and current research remains inconclusive regarding its impact on mosaicism, this study helped our clinic determine that conventional insemination does not lead to a higher incidence of mosaicism.

Previous studies have found that large follicles are associated with increased chance of obtaining blastocysts. The current findings suggest those blastocysts derived from large follicles are associated with delayed (day 6 or day 7) blastulation. This may hold implications regarding oogenesis and ovarian stimulation protocols, particularly in fresh transfer cycles.

While this study was not large enough to rule out weak correlations, it was reassuring to find the trigger agent, follicle size, cohort size, and day of blastocyst formation were not significantly correlated with embryo ploidy among these 344 biopsied blastocysts.

Naïve approaches to preparing training data for deep learning models for embryo ranking can create bias in the models. Our work illustrates the need for careful preparation of training data and monitoring of different metrics to identify and reduce potential sources of bias.

We developed a deep learning-based embryo ranking model that is broadly applicable and may reduce time to pregnancy by optimizing the order of embryo transfer.

Objective: Investigate any relationship between ovarian follicle size and the rate of good blastocyst formation among successfully fertilized (bipronuclear, or 2pn) oocytes.

The purpose of this study was to evaluate the effect of PGT on the predictive value of peri-implantation hCG levels for early pregnancy outcomes in vitrified-thawed single blastocyst transfers, including clinical pregnancy and ongoing pregnancy.

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requirement for data privacy restricts AI practitioners from accessing raw training data, meaning manual visual verification of private patient data is not possible.