ArticleReproductive biology and endocrinology : RB&E2021
An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.
Article in Reproductive biology and endocrinology : RB&E, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06762704 (A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy), which is not on this map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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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.
A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy: a Multi-Center, Prospective, Non-Randomized Validation Study
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- Morphokinetic parameter S2 predicts early embryo developmental potential and associated metabolic pathways.Journal of assisted reproduction and genetics · 2026Article
- Non-invasive assessment of embryo development and ploidy: morphokinetics predict blastulation, and exploratory metabolomics identifies candidate metabolic signatures for euploidy.Frontiers in cell and developmental biology · 2026Article
- Timelapse-based 3D reconstruction of blastocysts reveals 3D morphologies of human blastocysts.NPJ digital medicine · 2025Article
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- [Progress in the application of artificial intelligence in assisted reproduction].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- Time-Lapse Imaging in IVF: Bridging the Gap Between Promises and Clinical Realities.International journal of molecular sciences · 2025Review
- Review
- Large language models to facilitate pregnancy prediction after in vitro fertilization.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Review
- Preimplantation development analysis of aneuploid embryos with different chromosomal abnormalities.Heliyon · 2024Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- Article
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study.Reproductive biology and endocrinology : RB&E · 2024Article
- Multiple collapses of blastocysts after full blastocyst formation is an independent risk factor for aneuploidy - a study based on AI and manual validation.Reproductive biology and endocrinology : RB&E · 2024Observational
- A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning.Patterns (New York, N.Y.) · 2024Article
- Viewing early life without labels: optical approaches for imaging the early embryo†.Biology of reproduction · 2024Review
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.Reproductive biology and endocrinology : RB&E · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFor the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened.
methodsA total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process.
resultsThe euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80.
conclusionsThe TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.
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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.