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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: May 24, 2026

In Vitro Ovule Cultivation for Live-cell Imaging of Zygote Polarization and Embryo Patterning in Arabidopsis thaliana

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Published on: September 11, 2017

Non-Invasive Prediction of Embryo Ploidy from Time-Lapse Videos Using Video Vision Transformers (ViViT).

Teresa García-Navarro¹, Eduardo Alonso^1,2, Ane Goikolea-Vives^1,3

¹Vicomtech Foundation, Basque Research and Technology Alliance, San Sebastian, Spain.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

This study introduces a non-invasive AI tool using time-lapse embryo videos to predict ploidy, improving in vitro fertilization (IVF) success. The Video Vision Transformer (ViViT) model accurately identifies viable embryos, aiding clinical decisions.

Keywords:

Deep learning Embryo selection In vitro fertilisation (IVF)Ploidy prediction Time-lapse imaging Video Vision Transformer (ViViT)

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Area of Science:

Reproductive Medicine
Artificial Intelligence in Healthcare
Embryology

Background:

Accurate embryo selection is critical for successful in vitro fertilization (IVF).
Current methods like visual assessment and genetic testing have limitations in accuracy and invasiveness.
There is a need for non-invasive, objective tools to assess embryo viability.

Purpose of the Study:

To develop and validate a non-invasive method for predicting embryo ploidy using time-lapse videos.
To evaluate the performance of a Video Vision Transformer (ViViT) architecture for embryo assessment.
To provide an automated decision-support tool for improving embryo selection in IVF.

Main Methods:

Analysis of 2,388 embryo videos from non-donor IVF cycles.
Application of the Video Vision Transformer (ViViT) deep learning architecture.
Training and validation of a model to predict embryo ploidy (euploid, mosaic, carrier vs. aneuploid).

Main Results:

The ViViT-based model achieved a validation ROC-AUC of 0.85.
The model demonstrated strong ability to discriminate between eligible (euploid, mosaic, carrier) and ineligible (aneuploid) embryos.
Transformer-based video analysis captured dynamic developmental patterns effectively.

Conclusions:

Transformer-based video models offer a scalable, automated, and non-invasive approach for embryo selection in IVF.
This AI-driven method can assist clinicians in making more accurate decisions regarding embryo viability.
The ViViT architecture shows promise for improving IVF clinical outcomes through enhanced embryo assessment.