ArticleScientific reports2024
A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts.Journal of assisted reproduction and genetics · 2026Review
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- 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
- Machine learning and microfluidic integration for oocyte quality prediction.Scientific reports · 2025Article
- From the Understanding of Maternal Molecules and Mechanisms to Predicting Embryonic Development.Reproductive medicine and biologyReview
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART.
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