AIMC Topic: Pregnancy Rate

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Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.

Journal of assisted reproduction and genetics
Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture co...

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study.

Reproductive biology and endocrinology : RB&E
BACKGROUND: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to imp...

Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning.

JAMA network open
IMPORTANCE: Many indicators need to be considered when judging the condition of patients with infertility, which makes diagnosis and treatment complicated.

Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service.

JBRA assisted reproduction
Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obsta...

Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation.

Reproductive biomedicine online
RESEARCH QUESTION: Can a deep machine learning artificial intelligence algorithm predict ploidy and implantation in a known data set of static blastocyst images, and how does its performance compare against chance and experienced embryologists?

An artificial neural network for the prediction of assisted reproduction outcome.

Journal of assisted reproduction and genetics
PURPOSE: To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Journal of assisted reproduction and genetics
Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rate...

Pregnancy rate in water buffalo following fixed-time artificial insemination using new or used intravaginal devices with two progesterone concentrations.

Tropical animal health and production
This study evaluated the pregnancy rate (PR) after timed artificial insemination (TAI) in water buffalo (Bubalus bubalis) during both non-breeding and breeding season, using either a new or reused intravaginal device (IVD) with two different progeste...