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A machine learning-based score to predict live birth after mosaic embryo transfer.

José A Ortiz¹, Ruth Morales², Belén Lledó^2,3

¹Molecular Biology, Instituto Bernabeu, Alicante, Spain. jaortiz@institutobernabeu.com.

Journal of Assisted Reproduction and Genetics

|June 19, 2026

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An AI scoring system prioritizes mosaic embryos for better live birth rates. Embryo quality and biopsy day are key, with mosaicism degree playing a lesser role in assisted reproductive technology (ART) success.

Area of Science:

Reproductive Medicine
Genetics
Artificial Intelligence in Healthcare

Background:

Mosaic embryos, resulting from Preimplantation Genetic Testing for Aneuploidies (PGT-A), present a challenge in assisted reproductive technology (ART) due to uncertain implantation potential.
Accurate prioritization of mosaic embryos is crucial for improving live birth rates and optimizing ART outcomes.

Purpose of the Study:

To develop and validate an artificial intelligence (AI)-based scoring system for prioritizing mosaic embryos.
To identify key predictors of live birth outcomes in mosaic embryo transfers.

Main Methods:

A multicentre, retrospective study analyzed 264 mosaic embryos from 2583 PGT-A cycles.
Next-Generation Sequencing (NGS) was used for trophectoderm (TE) biopsy analysis of day-5 (D5) and day-6 (D6) blastocysts.

Keywords:

AI Live birth Machine learning Mosaicism

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Machine learning models were built using clinical, embryological, and laboratory variables to predict live birth, with refinement for a final scoring system.

Main Results:

Live birth rate for mosaic embryos was 36.36%.
The AI model identified embryo quality (42% weight) and biopsy day (34% weight) as the most significant predictors of live birth.
Mosaicism factors, including monosomy/trisomy (18%) and degree of mosaicism (6%), had lower predictive influence.

Conclusions:

Embryo quality is the primary determinant of success in mosaic embryo transfer.
Prioritizing high-quality, D5-biopsied embryos can improve live birth outcomes in ART.
The AI-derived score offers a novel approach to enhance decision-making in mosaic embryo selection.