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Cleavage and Blastulation

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After a large-single-celled zygote is produced via fertilization, the process of cleavage occurs while zygotes travel through the uterine tube. Cleavage is a mitotic cell division that does not result in growth. With each round of successive cell division, daughter cells get increasingly smaller.

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Morphometric Protocol for the Objective Assessment of Blastocyst Behavior During Vitrification and Warming Steps

Morphometric Protocol for the Objective Assessment of Blastocyst Behavior During Vitrification and Warming Steps

Published on: February 28, 2019

A novel system based on artificial intelligence for predicting blastocyst viability and visualizing the explanation.

Noritoshi Enatsu¹, Isao Miyatsuka², Le My An²

¹Hanabusa Women's Clinic Kobe Hyogo Japan.

Reproductive Medicine and Biology

|April 7, 2022

Summary

A new artificial intelligence (AI) system, Fertility Image Testing Through Embryo (FiTTE), enhances clinical pregnancy prediction from blastocyst images. FiTTE offers improved accuracy and visual explanations compared to traditional methods.

Keywords:

artificial intelligence assisted reproductive technology gradient‐weighted class activation mapping in vitro fertilization

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Last Updated: Sep 27, 2025

Morphometric Protocol for the Objective Assessment of Blastocyst Behavior During Vitrification and Warming Steps

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Human Blastocyst Biopsy and Vitrification

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Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

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Published on: August 10, 2022

Area of Science:

Reproductive medicine
Artificial intelligence in healthcare
Embryology

Background:

In vitro fertilization (IVF) success relies on accurate embryo assessment.
Conventional methods like Gardner scoring have limitations in predicting clinical pregnancy.
Novel AI approaches are needed to improve IVF outcomes.

Purpose of the Study:

To develop and evaluate an AI system, Fertility Image Testing Through Embryo (FiTTE), for predicting blastocyst viability.
To visualize AI predictions using gradient-based localization for enhanced interpretability.
To compare FiTTE's performance against the conventional Gardner scoring system.

Main Methods:

Retrospective analysis of 19,342 static blastocyst images from 9,961 infertile patients.
Training dataset comprised 17,984 single-blastocyst transfer cycles; testing dataset included 1,358 cycles.
Development of FiTTE, an AI model utilizing blastocyst images for prediction.

Main Results:

FiTTE achieved a prediction accuracy of 62.7% (AUC 0.68), outperforming Gardner scoring (59.8% accuracy, AUC 0.62).
An ensemble model combining image and clinical data reached 65.2% accuracy (AUC 0.71).
Visualization algorithm highlighted areas of blastocysts associated with clinical pregnancy.

Conclusions:

The novel AI system, FiTTE, offers more precise prediction of clinical pregnancy probability from blastocyst images.
FiTTE surpasses conventional Gardner scoring in predicting IVF success.
FiTTE provides interpretable AI predictions through visualized blastocyst images.