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Related Experiment Video

Updated: May 5, 2026

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
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Accurate machine learning model for human embryo morphokinetic stage detection.

Hooman Misaghi1, Lynsey Cree1, Nicholas Knowlton2,3

  • 1Department of Obstetrics, Gynaecology and Reproductive Sciences, University of Auckland, Auckland, New Zealand.

Journal of Assisted Reproduction and Genetics
|August 20, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately predicts 17 human embryo development stages, improving viability assessment. This tool automates analysis, standardizes processes, and reduces subjectivity in clinics.

Keywords:
Artificial IntelligenceDeep learningEmbryo morphokineticsMachine learningTime-lapse imaging

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Area of Science:

  • Reproductive biology
  • Artificial intelligence in medicine
  • Embryology

Background:

  • Accurate monitoring of human pre-implantation embryo development is crucial for assessing viability and implantation potential.
  • Existing tools for embryo analysis lack accuracy and accessibility, necessitating improved solutions.

Purpose of the Study:

  • To develop a highly accurate machine learning model for predicting 17 distinct morphokinetic stages of human pre-implantation development.
  • To provide a robust, automated tool for researchers and clinicians to standardize embryo analysis and reduce inter-clinic subjectivity.

Main Methods:

  • A computer vision model was developed using a large dataset of 273,438 labeled Embryoscope images.
  • Two deep learning architectures, EfficientNet-V2-Large with and without fertilization time input, were trained and evaluated.
  • A novel postprocessing algorithm was implemented to refine predictions and pinpoint exact morphokinetic stage transition times.

Main Results:

  • The model achieved an overall test F1-score of 0.881 and 87% accuracy across 17 morphokinetic stages on an independent dataset.
  • The proposed model demonstrated a 17% accuracy improvement over existing state-of-the-art models on the same dataset.

Conclusions:

  • The developed model accurately detects human embryo morphokinetic stages from static images.
  • The model precisely identifies the timing of stage changes within time-lapse videos, offering a significant advancement in embryo assessment.