AIMC Topic: Fertilization in Vitro

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Development of a single-center predictive model for conventional in vitro fertilization outcomes excluding total fertilization failure: implications for protocol selection.

Journal of ovarian research
OBJECTIVES: To develop a multidimensional clinical indicator-based prediction model for identifying high-risk patients with fertilization failure conventional in vitro fertilization (c-IVF) cycles, thereby optimizing therapeutic decision-making.

Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success.

NPJ biofilms and microbiomes
Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In ...

Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.

Nature communications
Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) m...

A potential supplemental indication of dipyridamole for contraception: Dipyridamole inhibits mouse sperm fertilization capacity in vitro.

Biochemical and biophysical research communications
The inhibition of sperm cAMP and ATP levels, using an FDA-approved medication, may impair sperm motility and, consequently, fertilization, thus paving the way for the development of a male contraceptive. The objective of this study was to define the ...

Artificial intelligence (AI) approaches to male infertility in IVF: a mapping review.

European journal of medical research
BACKGROUND: Male infertility contributes to 20-30% of infertility cases, yet traditional diagnostic and treatment methods face limitations in accuracy and consistency. Artificial intelligence (AI) promises to transform male infertility management wit...

Construction and evaluation of machine learning-based prediction model for live birth following fresh embryo transfer in IVF/ICSI patients with polycystic ovary syndrome.

Journal of ovarian research
OBJECTIVE: To investigate the determinants affecting live birth outcomes in fresh embryo transfer among polycystic ovary syndrome (PCOS) patients using various machine learning (ML) algorithms and to construct predictive models, offering novel insigh...

Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.

BMC pregnancy and childbirth
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...

A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.

Scientific reports
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 ...

Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency ...