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Related Experiment Video

Updated: May 13, 2026

Inducing Pseudopregnancy in Female Mice Without the Need for Vasectomized Males Prior to Non-Surgical Embryo Transfer or Artificial Insemination

Inducing Pseudopregnancy in Female Mice Without the Need for Vasectomized Males Prior to Non-Surgical Embryo Transfer or Artificial Insemination

Published on: July 7, 2023

Objective way to support embryo transfer: a probabilistic decision.

L Gianaroli¹, M C Magli, L Gambardella

¹SISMER, Reproductive Medicine Unit, via Mazzini 12, Bologna 40138, Italy. luca.gianaroli@sismer.it

Human Reproduction (Oxford, England)

|March 7, 2013

Summary

This summary is machine-generated.

A Bayesian network model effectively predicts IVF-ICSI pregnancy success using top-history embryos, female age, and insemination technique. This tool aids in selecting embryos for transfer to optimize live birth rates.

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Last Updated: May 13, 2026

Inducing Pseudopregnancy in Female Mice Without the Need for Vasectomized Males Prior to Non-Surgical Embryo Transfer or Artificial Insemination

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Published on: May 16, 2019

Area of Science:

Reproductive Medicine
In Vitro Fertilization (IVF)
Intracytoplasmic Sperm Injection (ICSI)

Background:

Estimating embryo viability and maternal receptivity is crucial for successful IVF-ICSI cycles.
Current predictive models for IVF-ICSI outcomes are limited, impacting treatment strategies.

Purpose of the Study:

To identify key factors influencing IVF-ICSI cycle outcomes.
To develop a reliable predictor for embryo implantation and pregnancy.

Main Methods:

Retrospective analysis of 388 fresh transfer IVF-ICSI cycles (2006-2008).
Evaluation of factors including female age, insemination technique, and embryo quality.
Utilized Decision Tree and Bayesian Network (BN) models for prediction.

Main Results:

Bayesian Network (BN) identified top-history embryos, female age, and insemination technique as significant predictors of pregnancy (AUC 0.72).
BN model also discriminated between no implantation, single, and twin implantations (AUCs 0.72, 0.64, 0.83).
Decision Tree model showed modest accuracy and was deemed clinically less useful.

Conclusions:

A BN model can reliably predict pregnancy and implantation outcomes in IVF-ICSI.
The model supports decisions on embryo selection and transfer numbers.
Further prospective studies are needed to validate the predictive model.