ArticleAJOG global reports2022
Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles.
Article in AJOG global reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Factors associated with embryo mosaicism: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2023Pooled it
- "Old and new" frontiers in embryo viability assessment: current practices and introduction of emerging approaches.Biology of reproduction · 2026Review
- Pregnancy outcomes prediction performance of a scoring system based on maternal immunological parameters in patients with unexplained recurrent pregnancy loss: a monocentric retrospective study.BMC pregnancy and childbirth · 2026Article
- Changes in sequencing technology used for preimplantation genetic testing for aneuploidy.Journal of assisted reproduction and genetics · 2025Review
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025Review
- Quality of IVM ovarian tissue oocytes: impact of clinical, demographic, and laboratory factors.Journal of assisted reproduction and genetics · 2024Article
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.Reproductive biology and endocrinology : RB&E · 2024Article
- Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.Reproductive biology and endocrinology : RB&E · 2024Observational
- Criteria for implementing artificial intelligence systems in reproductive medicine.Clinical and experimental reproductive medicine · 2024Article
- The Prediction of IVF Outcomes with Autologous Oocytes and the Optimal MII Oocyte/Embryo Number for Live Birth at Advanced Maternal Age.Medicina (Kaunas, Lithuania) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe factors associated with embryo aneuploidy have been extensively studied. Mostly maternal age and to a lesser extent male factor and ovarian stimulation have been related to the occurrence of chromosomal alterations in the embryo. On the other hand, the main factors that may increase the incidence of embryo mosaicism have not yet been established.
objectiveThis study aimed to establish a machine learning model that would allow prediction of aneuploidies and mosaicism in embryos conceived via in vitro fertilization, and thus help to determine which variables are associated with these chromosomal alterations. STUDY
designThe study design was observational and retrospective. A total of 6989 embryos from 2476 cycles of preimplantation genetic testing for aneuploidies were included (January 2013 to December 2020). The trophoectoderm biopsies on day-5, -6, or -7 blastocysts were analyzed by preimplantation genetic testing for aneuploidies (PGT-A). The different maternal, paternal, couple, embryo, and in vitro fertilization cycle characteristics were recorded in a database (22 predictor variables) from which predictive models of embryo aneuploidy and mosaicism were developed; 16 different unsupervised classification machine learning algorithms were used to establish the predictive models.
resultsTwo different predictive models were performed: one for aneuploidy and the other for mosaicism. The predictor variable was of multiclass type because it included the segmental- and whole-chromosome alteration categories. The best predicting models for both aneuploidies and mosaicism were those obtained from the Random Forest algorithm. The area under ROC curve (AUC) value was 0.792 for the aneuploidy explanatory model and 0.776 for mosaicism. The most important variable in the final aneuploidy model was maternal age, followed by paternal and maternal karyotype and embryo quality. In the predictive model of mosaicism, the most important variable was the technique used in preimplantation genetic testing for aneuploidies and embryo quality, followed by maternal age and day of biopsy.
conclusionIt is possible to predict embryo aneuploidy and mosaicism from certain characteristics of the patients and their embryos.
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