AIMC Topic: Ovulation Induction
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Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes.
Fertility and sterility
May 19, 2023
Because of the birth of the first baby after in vitro fertilization (IVF), the field of assisted reproductive technologies (ARTs) has seen significant advancements in the past 40 years. Over the last decade, the healthcare industry has increasingly a...
Artificial intelligence and assisted reproductive technologies: 2023. Ready for prime time? Or not.
Fertility and sterility
May 18, 2023
Artificial intelligence has transformed many aspects of health care from image analysis to clinical decision making. Its evolution in medicine has been gradual and deliberate with several unanswered questions regarding efficiency, privacy, and bias. ...
Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.
Reproductive biomedicine online
Jul 28, 2022
RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian ...
The Comparison of Fixed and Flexible Progestin Primed Ovarian Stimulation on Mature Oocyte Yield in Women at Risk of Premature Ovarian Insufficiency.
Frontiers in endocrinology
Feb 3, 2022
While gonadotrophin releasing hormone (GnRH) antagonists have been the standard of pituitary suppression during ovarian stimulation for ART, progestin primed ovarian stimulation (PPOS) has emerged as an alternative. Progestins can be started simultan...
An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions.
Reproductive biomedicine online
Oct 20, 2021
RESEARCH QUESTION: Can workflow during IVF be facilitated by artificial intelligence to limit monitoring during ovarian stimulation to a single day and enable level-loading of retrievals?
A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.
Fertility and sterility
Jul 10, 2021
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.
Therapy, Computer-Assisted
Sperm Injections, Intracytoplasmic
Infertility
Blastocyst
Fertility
Ovulation Induction
Oocyte Retrieval
Fertility Agents, Female
Adult
Male
Time Factors
Treatment Outcome
Decision Trees
Embryo Transfer
Pregnancy Outcome
Clinical Decision-Making
Pregnancy
Female
Humans
Machine Learning
Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization.
Fertility and sterility
Oct 1, 2020
OBJECTIVE: To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm's accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by ...
Man versus machine in in vitro fertilization-can artificial intelligence replace physicians?
Fertility and sterility
Aug 17, 2020
Machine learning vs. classic statistics for the prediction of IVF outcomes.
Journal of assisted reproduction and genetics
Aug 11, 2020
PURPOSE: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.
Anti-Müllerian hormone concentrations and antral follicle counts for the prediction of pregnancy outcomes after intrauterine insemination.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Dec 18, 2015
OBJECTIVE: To evaluate anti-Müllerian hormone (AMH) concentrations and antral follicle counts (AFCs) in the prediction of pregnancy outcomes after controlled ovarian stimulation among women undergoing intrauterine insemination.
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