AIMC Topic: Ovulation Induction
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A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.
Scientific reports
Mar 6, 2025
Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or female reproductive system, presents a global challenge due to its impact on one in six individuals worldwide. Given the high prevalence of infertility ...
Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.
Nature communications
Jan 8, 2025
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'ru...
Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments.
Journal of gynecology obstetrics and human reproduction
Dec 27, 2024
In-vitro fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrat...
Machine learning tool for predicting mature oocyte yield and trigger day from start of stimulation: towards personalized treatment.
Reproductive biomedicine online
Sep 4, 2024
RESEARCH QUESTION: Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?
Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study.
Scientific reports
Aug 20, 2024
The objective of this study was to evaluate clinical outcomes for patients undergoing IVF treatment where an artificial intelligence (AI) platform was utilized by clinicians to help determine the optimal starting dose of FSH and timing of trigger inj...
Optimizing trigger timing in minimal ovarian stimulation for In Vitro fertilization using machine learning models with random search hyperparameter tuning.
Computers in biology and medicine
Jul 24, 2024
Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ovarian s...
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.
Clinical data-based modeling of IVF live birth outcome and its application.
Reproductive biology and endocrinology : RB&E
Jul 8, 2024
BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, thi...
Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.
Journal of medical Internet research
Jul 5, 2024
BACKGROUND: In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response ...
Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles.
Fertility and sterility
Jul 23, 2023
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.
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