AIMC Topic: Embryonic Development

Clear Filters Showing 1 to 10 of 69 articles

Fifty high-content light sheet fluorescence microscopy datasets of Tribolium castaneum embryogenesis.

Scientific data
The red flour beetle (Tribolium castaneum) is a key model organism in developmental biology, genetics, and agricultural research. To address the limited availability of high-quality microscopy data documenting its embryonic morphogenesis, we assemble...

Blastulation and ploidy prediction using morphology assessment in 33,999 day-3 embryos.

Scientific reports
Although contemporary practice in in vitro fertilization (IVF) favors embryo transfer at blastocyst stage, several centres worldwide employ cleavage-stage Day-3 embryo transfers. The advantage of cultures extended to Day-5 and beyond, is to ensure th...

Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.

Reproductive biology and endocrinology : RB&E
BACKGROUND: The application of Artificial Intelligence (AI) to sperm selection during Intracytoplasmic Sperm Injection (ICSI) procedures represents one of the most innovative advances in assisted reproductive technology (ART). Traditional sperm selec...

Deep learning methods to forecasting human embryo development in time-lapse videos.

PloS one
BACKGROUND: In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, eith...

Early embryo development: the current perspective in molecular evaluation and clinical status.

Systems biology in reproductive medicine
Early embryo development and competence mechanisms are paramount to ART's success but are still underexplored in human-relevant animal models. Clinical embryo evaluation remains largely based on subjectively evaluated morphological characteristics. I...

Deep manifold learning reveals hidden developmental dynamics of a human embryo model.

Science advances
In this study, postimplantation human epiblast and amnion development are modeled using a stem cell-based embryoid system. A dataset of 3697 fluorescent images, along with tissue, cavity, and cell masks, is generated from experimental data. A computa...

Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.

Nature communications
Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of tradition...

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...

Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle.

Scientific reports
Evaluating cumulus-oocyte complex (COC) morphology is commonly used to assess oocyte quality. However, clear guidelines on interpreting COC morphology data are lacking as this evaluation method is subjective. In the present study, individual in vitro...

Innovative AI models for clinical decision-making: predicting blastocyst formation and quality from time-lapse embryo images up to embryonic day 3.

Computers in biology and medicine
Accurate embryo assessment on embryonic day 3 of assisted reproductive technology (ART) is crucial for deciding whether to continue the culture until day 5 (blastocyst stage) or opt for earlier transfer or cryopreservation. Prolonged culture often im...