ReviewContraception and reproductive medicine2025
Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta- analysis of artificial intelligence in embryo assessment.
Review in Contraception and reproductive medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2026Article
- Embryo selection using artificial intelligence: moving from validation to responsible clinical integration.Journal of assisted reproduction and genetics · 2026Article
- A multiday machine learning framework based on improved-NEQsi for predicting embryo quality and pregnancy outcomes in IVF.Journal of assisted reproduction and genetics · 2026Article
- Modern Polycystic Ovary Syndrome (PCOS) Management: Intelligent Drug Delivery and Metabolic Reprogramming for Ovarian Restoration and Fertility Optimization.Biomolecules · 2026Review
- Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images.Medicina (Kaunas, Lithuania) · 2026Article
- Non-invasive biomarkers in assisted reproductive technologies, current evidence, clinical utility and future perspectives.Frontiers in reproductive health · 2026Review
- Non-Invasive Extracellular Vesicle Biomarkers in Endometriosis, Molecular Signatures Linking Pelvic Inflammation, Oocyte Quality, and IVF OutcomesCurrent issues in molecular biology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEmbryo selection remains a key challenge in in vitro fertilization (IVF), as many morphologically "normal" embryos fail to implant. Artificial intelligence (AI) offers a promising tool for improving embryo assessment by providing more objective and accurate predictions of pregnancy outcomes. This study aims to systematically review and conduct a diagnostic meta-analysis to evaluate the effectiveness of AI-based tools in embryo selection for predicting pregnancy outcomes in IVF.
methodsWe conducted a systematic review following PRISMA guidelines, searching Web of Science, Scopus, and PubMed. Original research articles evaluating AI's diagnostic accuracy in embryo selection were included, while duplicates, non-peer-reviewed papers, abstracts, and conference proceedings were excluded. Data on sample sizes, AI tools, and diagnostic metrics were extracted, with quality assessed using the QUADAS-2 tool.
resultsAI-based embryo selection methods showed strong diagnostic performance, with pooled sensitivity of 0.69 and specificity of 0.62 in predicting implantation success. The positive likelihood ratio was 1.84 and the negative likelihood ratio was 0.5. The area under the curve reached 0.7, indicating high overall accuracy. The Life Whisperer AI model achieved 64.3% accuracy in predicting clinical pregnancy, while the FiTTE system, which integrates blastocyst images with clinical data, improved prediction accuracy to 65.2% with an AUC of 0.7.
conclusionAI offers a promising advancement in embryo selection for IVF, with the potential to enhance clinical outcomes and improve decision-making. Future studies should focus on refining these models to achieve the ultimate goal of a healthy live birth by developing more sophisticated algorithms and validating them with larger, diverse datasets.
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