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Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle.

Annelies Raes1, Danilo Babin2, Osvaldo Bogado Pascottini3,4

  • 1Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium. Annelrae.Raes@UGent.be.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) models accurately predict bovine oocyte quality by analyzing cumulus-oocyte complex (COC) morphology, outperforming human embryologists. These AI tools offer an objective method for assessing oocyte developmental competence in the lab.

Keywords:
Artificial intelligenceBovine oocyte qualityCumulus-oocyte complexEmbryo developmentIn vitro embryo productionMorphology

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Area of Science:

  • Veterinary Science
  • Embryology
  • Artificial Intelligence in Biology

Background:

  • Cumulus-oocyte complex (COC) morphology assessment is crucial for oocyte quality evaluation.
  • Current subjective methods for COC morphology interpretation lack standardized guidelines and exhibit limitations.

Purpose of the Study:

  • To identify key morphological features differentiating COC quality.
  • To evaluate human embryologists' predictive accuracy in assessing COC morphology.
  • To develop artificial intelligence (AI) models for predicting oocyte developmental competence.

Main Methods:

  • Immature bovine COCs were evaluated using individual in vitro embryo production.
  • Embryologists and two AI models (deep neural network and random forest) analyzed COC images.
  • Predictive performance was assessed using balanced accuracy.

Main Results:

  • Cumulus size was identified as a critical feature for COC quality by AI, contrasting with embryologists' focus on ooplasm morphology.
  • Human assessment showed low predictive ability (42.9% balanced accuracy) and fair reliability.
  • AI models significantly outperformed embryologists, achieving 79.3% (DNN) and 71.2% (RF) balanced accuracy.

Conclusions:

  • Novel AI models successfully predict the developmental competence of immature bovine oocytes.
  • AI provides an objective and superior alternative to subjective human assessment of COC morphology.
  • AI tools can enhance decision-making processes in embryology laboratories.