ArticleReproductive biology and endocrinology : RB&E2024
Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.
Article in Reproductive biology and endocrinology : RB&E, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Building and Application of a Machine Learning Model on Predicting Euploid Blastocysts in IVF Treatments Without PGT-A.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- A robust clinical-laboratory AI model for predicting cumulative live birth per oocyte retrieval as a benchmark for evaluating emerging embryo selection technologies.JBRA assisted reproduction · 2026Article
- Development and validation of a predictive model for early blastocyst formation on day 4 post-fertilization.Frontiers in endocrinology · 2026Article
- Prediction of human embryo ploidy based on morphokinetic parameters: Investigating the use of time-lapse imaging for non-invasive embryo selection.JBRA assisted reproduction · 2025Article
- Deep Learning Models: Their Relationship with Embryonic Euploidies and Reproductive Outcomes.Genes · 2025Review
- Review
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025Review
- Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency.Biology · 2024Review
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?Frontiers in endocrinology · 2024Article
- Time-Lapse Culture Improves Development and Pregnancy Outcomes of Sibling Normally Fertilized Oocytes: A Prospective Multicenter Study.Reproductive medicine and biologyArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.
methodsIn present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.
resultsSignificant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.
conclusionsThis study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.
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