AIMC Topic: Embryo Transfer
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A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.
Scientific reports
Nov 14, 2024
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer o...
Prediction of clinical pregnancy outcome after single fresh blastocyst transfer during in vitro fertilization: an ensemble learning perspective.
Human fertility (Cambridge, England)
Nov 11, 2024
To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 patients (492 in training set and 123 in test set) who underwent the first single and fresh blastocy...
External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos.
Fertility and sterility
Oct 15, 2024
OBJECTIVE: To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.
Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.
Reproductive biology and endocrinology : RB&E
Sep 11, 2024
BACKGROUND: Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorit...
Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?
Frontiers in endocrinology
Aug 29, 2024
PURPOSE: With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in ...
Preovulatory progesterone levels are the top indicator for ovulation prediction based on machine learning model evaluation: a retrospective study.
Journal of ovarian research
Aug 21, 2024
BACKGROUND: Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics...
Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.
Nature medicine
Aug 9, 2024
To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Euro...
Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.
Reproductive biology and endocrinology : RB&E
Aug 8, 2024
PURPOSE: To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.
Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.
Chinese medical journal
Jul 12, 2024
BACKGROUND: In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous emb...
The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.
Reproductive biology and endocrinology : RB&E
Jul 10, 2024
OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.
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