ArticleScientific reports2023
Development and validation of deep learning based embryo selection across multiple days of transfer.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- From time-lapse to morphokinetics: neural ODE dynamics for reliable embryo stage transition timing.Journal of assisted reproduction and genetics · 2026Article
- Automation in the IVF laboratory in 2026: four technology domains, an asymmetric evidence base, and the case for parallel development with clinician-side automation.Journal of assisted reproduction and genetics · 2026Review
- Embryologist experience affects concordance with an artificial intelligence embryo ranking algorithm: benefit of artificial intelligence assistance.F&S reports · 2026Article
- Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection.NPJ digital medicine · 2026Article
- Observational
- Multi-omics biomarkers in female fertility: from oocyte quality to endometrial receptivity and clinical translation.Biomarker research · 2026Review
- Stability and reliability of artificial intelligence models in embryo selection for in vitro fertilization.Fertility and sterility · 2026Article
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Blastulation and ploidy prediction using morphology assessment in 33,999 day-3 embryos.Scientific reports · 2025Article
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- Accurate machine learning model for human embryo morphokinetic stage detection.Journal of assisted reproduction and genetics · 2025Article
- Correlation of fetal heartbeat outcome after Day 3 or Day 5 single embryo transfer of morphologically selected embryos with an annotation-free deep learning scoring system: Results from a multi-center study.Journal of assisted reproduction and genetics · 2025Observational
- ESHRE's key research priorities in infertility: maximizing impact on science, people and society†.Human reproduction (Oxford, England) · 2025Article
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- Deep Learning Models: Their Relationship with Embryonic Euploidies and Reproductive Outcomes.Genes · 2025Review
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Machine learning approaches for image classification in developmental biology and clinical embryology.Development (Cambridge, England) · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.
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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.