Observational studyJournal of assisted reproduction and genetics2026
A machine learning-based score to predict live birth after mosaic embryo transfer.
Observational study in Journal of assisted reproduction and genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to develop an artificial intelligence-based scoring system to prioritize mosaic embryos according to live birth outcomes.
methodsThis 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.
resultsAmong 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.