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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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Predicting pregnancy rate following multiple embryo transfers using algorithms developed through static image

Yun Tian¹, Wei Wang², Yabo Yin¹

¹College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.

Reproductive Biomedicine Online

|February 26, 2017

Summary

This study introduces a new algorithm using local binary patterns and Adaboost classifiers to predict pregnancy rates from embryo images. The method improves embryo selection for better in-vitro fertilization success.

Keywords:

IVF–ET embryo image potential prediction transfer cycles

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Area of Science:

Reproductive Medicine
Medical Imaging Analysis
Computational Biology

Background:

Single-embryo image assessment is often inaccurate due to imprecise labeling.
Current methods struggle to reliably predict embryo implantation potential.

Purpose of the Study:

To develop a novel algorithm for predicting pregnancy rates by analyzing entire embryo transfer cycles.
To improve embryo selection for assisted reproductive technologies.

Main Methods:

Feature extraction using the local binary pattern (LBP) operator on embryo images.
Utilizing Real Adaboost, Gentle Adaboost, and Modest Adaboost classifiers to predict pregnancy rate.
Employing a majority vote rule based on the three Adaboost classifiers for final prediction.

Main Results:

The proposed algorithm demonstrated good predictive performance for pregnancy rates.
The method considers multiple embryo images within a transfer cycle as a single entity.

Conclusions:

The novel algorithm can assist embryologists and clinicians in selecting embryos for transfer.
This approach has the potential to improve overall pregnancy rates in fertility treatments.