BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine ...
RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?
OBJECTIVE: To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based meth...
Reproductive biology and endocrinology : RB&E
39118049
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.
BACKGROUND: Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of ...
Medical science monitor : international medical journal of experimental and clinical research
39707645
BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, a...
RESEARCH QUESTION: Can machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?
Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 end...
OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of m...