AIMC Topic: Blastocyst

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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...

An intra- and inter-class context and consistency network for supervised and semi-supervised blastocyst segmentation.

Scientific reports
The implantation potential of an embryo is intricately linked to the quality of its blastocyst. Consequently, achieving an objective and precise identification of blastocyst morphology is imperative. The purpose of this study is to focus on the struc...

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...

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...

Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency ...

Challenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions.

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological interpretation and allocation decisions driven by biopsy findings. With the increasing use of artifi...

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

Scientific reports
The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the l...