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Cleavage and Blastulation

Cleavage and Blastulation

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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Related Experiment Video

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Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

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Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and

Aisling Ahlström¹, Jørgen Berntsen², Martin Johansen²

¹IVIRMA Global Research Alliance, Livio Gothenburg, Sweden.

Reproductive Biomedicine Online

|October 22, 2023

Summary

The deep learning model iDAScore v2.0 accurately correlates with manual embryo assessment for cell number and fragmentation. This AI tool shows predictive value for live birth outcomes in assisted reproductive technology.

Keywords:

deep learning algorithm embryo selection iDAScore live birth morphology time-lapse

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Area of Science:

Embryology
Artificial Intelligence in Medicine
Reproductive Medicine

Background:

Manual assessment of cleavage-stage embryos is crucial for assisted reproductive technology (ART) success.
Evaluating embryo cell number and fragmentation is a standard morphological assessment.
Objective, automated evaluation methods are needed to complement subjective manual scoring.

Purpose of the Study:

To investigate the correlation between manual assessment of embryo cell number and fragmentation and the iDAScore v2.0 deep learning algorithm.
To evaluate the predictive value of iDAScore v2.0 for live birth outcomes.

Main Methods:

A retrospective observational study analyzed 5040 embryos from 1786 ART treatments.
Embryo evaluation by iDAScore v2.0 was compared with manual scoring of cell number and fragmentation.
Data from fresh single embryo transfers on days 2 and 3 post-fertilization were used.

Main Results:

iDAScore v2.0 significantly correlated with manual assessments of cell number and fragmentation (P < 0.001).
Lower cell numbers and higher fragmentation correlated with lower iDAScore values.
iDAScore demonstrated a predictive value for live birth, comparable to traditional morphological assessment (AUC 0.607-0.627).

Conclusions:

The iDAScore v2.0 deep learning model shows significant correlation with manual evaluation of cleavage-stage embryos.
iDAScore v2.0 offers a valuable, objective tool for embryo assessment in ART.
The algorithm has potential predictive value for live birth when integrated with morphological assessment.