AIMC Topic: Pregnancy Rate

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Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

Frontiers in endocrinology
OBJECTIVE: Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients se...

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

Journal of assisted reproduction and genetics
PURPOSE: This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese popula...

Clinical outcomes of single blastocyst transfer with machine learning guided noninvasive chromosome screening grading system in infertile patients.

Reproductive biology and endocrinology : RB&E
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...

Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rate.

Reproductive biomedicine online
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?

Seeking arrangements: cell contact as a cleavage-stage biomarker.

Reproductive biomedicine online
RESEARCH QUESTION: What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos?

Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network.

Reproductive biomedicine online
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?

Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study.

Reproductive biomedicine online
RESEARCH QUESTION: What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial?

Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study.

Reproductive biomedicine online
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?

Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles.

Fertility and sterility
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.

Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection.

Fertility and sterility
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...