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Mouse Oocyte Microinjection, Maturation and Ploidy Assessment
Published on: July 23, 2011
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Artificial intelligence unlocks oocyte competence: MAGENTA outperforms clinical predictors and boosts euploidy
Amanda Setti1, Daniela Braga2, Maite Del Collado2
1Scientific Research Department, Sapientiae Institute, Centro de Estudos e Pesquisa em Reprodução Humana Assistida, São Paulo, SP, Brazil.
F&S Science
|March 13, 2026
Summary
An AI-derived oocyte morphology score (MAGENTA™) shows independent associations with fertilization, embryo development, and euploidy. Integrating MAGENTA™ with clinical and embryological data significantly enhances prediction accuracy for embryo euploidy.
Area of Science:
- Reproductive Medicine
- Artificial Intelligence in Healthcare
- Embryology
Background:
- Oocyte morphology is crucial for IVF success.
- Predicting embryo euploidy remains a challenge in assisted reproductive technologies.
- AI offers novel tools for objective morphological assessment.
Purpose of the Study:
- To evaluate the predictive capability of the AI-derived MAGENTA™ score for oocyte and embryo outcomes.
- To determine if MAGENTA™ improves euploidy prediction models when combined with other variables.
- To identify factors influencing MAGENTA™ scores.
Main Methods:
- Retrospective cohort study of 2,196 patients undergoing ICSI.
- Analysis of 13,370 oocytes scored using MAGENTA™ prior to ICSI.
- Development and evaluation of predictive models (MAGENTA-only, MAGENTA™ + morphology, full model) for euploidy using ROC analysis and SHAP values.
Main Results:
- MAGENTA™ scores positively correlated with fertilization, blastulation, and euploidy (p < 0.001).
- Female age was the strongest negative predictor of MAGENTA™ (B = -0.063/year; p < 0.001).
- The full model integrating MAGENTA™, embryo morphology, and clinical data achieved the highest euploidy prediction accuracy (AUC 0.666).
Conclusions:
- MAGENTA™ is an independent predictor of key IVF outcomes, including embryo euploidy.
- Combining MAGENTA™ with embryo morphology and clinical data significantly enhances euploidy prediction performance.
- AI-driven oocyte assessment holds promise for improving IVF success rates.
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