AIMC Topic: Embryonic Development
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An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes.
Reproductive biomedicine online
Jan 18, 2024
RESEARCH QUESTION: Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes?
Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro.
Computers in biology and medicine
Dec 1, 2023
Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a...
Uncovering developmental time and tempo using deep learning.
Nature methods
Nov 23, 2023
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very chal...
Multi-batch single-cell comparative atlas construction by deep learning disentanglement.
Nature communications
Jul 12, 2023
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield ...
Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos.
Reproductive biomedicine online
Aug 13, 2022
RESEARCH QUESTION: What is the association between the deep learning-based scoring system, iDAScore, and biological events during the pre-implantation period?
Stain-free detection of embryo polarization using deep learning.
Scientific reports
Feb 14, 2022
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...
Time-Lapse Systems: A Comprehensive Analysis on Effectiveness.
Seminars in reproductive medicine
Jan 10, 2022
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...
Deep learning is widely applicable to phenotyping embryonic development and disease.
Development (Cambridge, England)
Nov 5, 2021
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...
Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm.
Frontiers in endocrinology
Nov 2, 2021
fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factor...
Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.
Journal of assisted reproduction and genetics
Apr 27, 2021
Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture co...
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