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In Vitro Fertilization

In Vitro Fertilization

In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...

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Related Experiment Video

Updated: Aug 15, 2025

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

Published on: June 7, 2013

New frontiers in embryo selection.

Isaac Glatstein¹, Alejandro Chavez-Badiola^2,3,4, Carol Lynn Curchoe⁵

¹Conceive NJ, Wall Township, NJ, 07727, USA. iglatstein@conceivenj.com.

Journal of Assisted Reproduction and Genetics

|January 7, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing assisted reproductive technology (ART) by automating embryo selection, a crucial step in in vitro fertilization (IVF). This AI-driven approach promises increased reliability, reproducibility, and higher pregnancy rates for infertile couples worldwide.

Keywords:

AI Artificial intelligence Embryo ranking Embryo selection Machine learning

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Last Updated: Aug 15, 2025

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Published on: October 19, 2021

Area of Science:

Reproductive Medicine
Medical Technology
Artificial Intelligence in Healthcare

Background:

Human infertility affects 1 in 6 couples globally, necessitating advanced reproductive technologies.
Assisted reproductive technology (ART) cycles are increasing, highlighting the need for improved efficiency and success rates.
Current embryo selection methods in IVF are manual, subjective, and lack reproducibility.

Purpose of the Study:

To review the current landscape of embryo selection methodologies in assisted reproduction.
To explore the transformative potential of artificial intelligence (AI) in improving IVF processes.
To highlight AI's role in automating and enhancing embryo evaluation and selection for better outcomes.

Main Methods:

Review of existing literature on assisted reproductive technology and AI applications in IVF.
Analysis of AI platforms developed for embryo evaluation, performance assessment, and outcome prediction.
Comparison of AI-driven digital embryo selection with traditional manual methods.

Main Results:

AI demonstrates significant utility across various IVF laboratory functions, including quality assurance and performance evaluation.
AI systems can predict embryo ploidy, implantation, fetal heartbeat, and live birth outcomes with increasing accuracy.
AI facilitates a shift from manual embryo selection to a reliable, reproducible, and automated digital paradigm.

Conclusions:

AI technology for automated embryo evaluation and selection has matured, offering a superior alternative to current practices.
AI integration is poised to become the new standard in IVF laboratories, enhancing success rates and clinic efficiency.
AI-driven advancements in ART are crucial for addressing global infertility challenges and improving patient outcomes.