American journal of obstetrics and gynecology
37116822
OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.
Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplas...
RESEARCH QUESTION: What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?
OBJECTIVE: To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles.
RESEARCH QUESTION: Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory?
RESEARCH QUESTION: What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial?
RESEARCH QUESTION: Could EMBRYOLY, an artificial intelligence embryo evaluation tool, assist embryologists to increase first cycle pregnancy rate and reduce cycles to pregnancy for patients?
Reproductive biology and endocrinology : RB&E
38783347
BACKGROUND: Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificial intelligence (NICS-AI) grad...