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Purpose <p>This study aimed to develop an artificial intelligence–based scoring system to prioritize mosaic embryos according to live birth outcomes.</p> Methods <p>This multicentre, observational, retrospective study included 264 transferred mosaic embryos from 2583 PGT-A (Preimplantation Genetic Testing for Aneuploidies) cycles performed between January 2017 and January 2023. Trophectoderm (TE) biopsies from day-5 (D5) or day-6 (D6) blastocysts were analysed using Next-Generation Sequencing (NGS) (VeriSeq, Illumina®, San Diego, CA, USA). Biopsied embryos were vitrified and subsequently transferred. Clinical, embryological, and laboratory variables were collected to build predictive machine learning models for live birth. Models excluding cohort-invariant variables were refined to derive the final scoring system.</p> Results <p>Among mosaic embryos, biochemical, clinical pregnancy, and live birth rates were 50.75%, 41.66%, and 36.36%, respectively. The best-performing model, validated through tenfold cross-validation, identified embryo quality and biopsy day as the most influential predictors (42% and 34% weights, respectively), while mosaicism-related factors such as monosomy/trisomy (18%) and mosaicism degree (6%) had lower influence. The resulting score suggests prioritizing high-quality embryos biopsied on D5, as the type and level of mosaicism play a minor role in gestational potential, except when comparing embryos of similar quality where lower mosaicism levels and absence of monosomy are advantageous.</p> Conclusion <p>The AI-derived score highlights embryo quality as the primary determinant of success in mosaic embryo transfer, supporting prioritization of high-quality, D5-biopsied embryos to improve live birth outcomes in ART.</p>

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

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • José A. Ortiz,
  • Ruth Morales,
  • Belén Lledó,
  • Francisca M. Lozano,
  • Alba M. Cascales,
  • Jorge Ten,
  • Juan C. Castillo,
  • Andrea Bernabeu

摘要

Purpose

This study aimed to develop an artificial intelligence–based scoring system to prioritize mosaic embryos according to live birth outcomes.

Methods

This multicentre, observational, retrospective study included 264 transferred mosaic embryos from 2583 PGT-A (Preimplantation Genetic Testing for Aneuploidies) cycles performed between January 2017 and January 2023. Trophectoderm (TE) biopsies from day-5 (D5) or day-6 (D6) blastocysts were analysed using Next-Generation Sequencing (NGS) (VeriSeq, Illumina®, San Diego, CA, USA). Biopsied embryos were vitrified and subsequently transferred. Clinical, embryological, and laboratory variables were collected to build predictive machine learning models for live birth. Models excluding cohort-invariant variables were refined to derive the final scoring system.

Results

Among mosaic embryos, biochemical, clinical pregnancy, and live birth rates were 50.75%, 41.66%, and 36.36%, respectively. The best-performing model, validated through tenfold cross-validation, identified embryo quality and biopsy day as the most influential predictors (42% and 34% weights, respectively), while mosaicism-related factors such as monosomy/trisomy (18%) and mosaicism degree (6%) had lower influence. The resulting score suggests prioritizing high-quality embryos biopsied on D5, as the type and level of mosaicism play a minor role in gestational potential, except when comparing embryos of similar quality where lower mosaicism levels and absence of monosomy are advantageous.

Conclusion

The AI-derived score highlights embryo quality as the primary determinant of success in mosaic embryo transfer, supporting prioritization of high-quality, D5-biopsied embryos to improve live birth outcomes in ART.