AIMC Topic: Fertilization in Vitro

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Blastulation and ploidy prediction using morphology assessment in 33,999 day-3 embryos.

Scientific reports
Although contemporary practice in in vitro fertilization (IVF) favors embryo transfer at blastocyst stage, several centres worldwide employ cleavage-stage Day-3 embryo transfers. The advantage of cultures extended to Day-5 and beyond, is to ensure th...

Predicting IVF outcomes using a logistic regression-ABC hybrid model: A proof-of-concept study on supplement associations.

PloS one
Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression-Ar...

Machine learning-based preliminary screening tool for clinical pregnancy prediction: towards management of IVF/ICSI stages.

Annals of medicine
BACKGROUND: Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely...

Multidimensional trophoblast invasion assessment by combining 3D in vitro modeling and deep learning analysis.

NPJ systems biology and applications
Infertility affects millions of couples worldwide, and in vitro fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ~60% of patients, highlighting the need for innovative solutions to...

Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.

European journal of medical research
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...

MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.

Scientific reports
The need to reduce the number of embryos transferred in assisted reproductive care to prevent multiple gestations has led to a stronger emphasis on selecting embryos with the highest morphological quality. Although this evaluation has traditionally b...

Machine learning and microfluidic integration for oocyte quality prediction.

Scientific reports
Despite advancements in in vitro fertilization (IVF) over the past 30 years, its outcome effectiveness remains low (20-40%). This study introduces a microfluidic-based machine learning framework to improve predictive accuracy in oocyte quality assess...

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.

Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.

Scientific reports
Predicting blastocyst formation poses significant challenges in reproductive medicine and critically influences clinical decision-making regarding extended embryo culture. While previous research has primarily focused on determining whether an IVF cy...

Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle.

Scientific reports
Evaluating cumulus-oocyte complex (COC) morphology is commonly used to assess oocyte quality. However, clear guidelines on interpreting COC morphology data are lacking as this evaluation method is subjective. In the present study, individual in vitro...