ArticleeLife2020
Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 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-Driven Reproductive Bioengineering: Integrating Fertility Diagnostics, Organ-on-Chip Systems, Cryobiology and Epigenetic Safety for Precision Reproductive Medicine.Bioengineering (Basel, Switzerland) · 2026Review
- An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning.Journal of biomedical physics & engineering · 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
- Automation, Artificial Intelligence (AI), and Digital Management in IVF Laboratories: Current Status, Challenges and Potential.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- Evaluating the concordance between AI-based and conventional embryo selection: implications for clinical decision-making.Reproductive biomedicine online · 2026Article
- Multi-omics biomarkers in female fertility: from oocyte quality to endometrial receptivity and clinical translation.Biomarker research · 2026Review
- AI prediction models based on time-lapse imaging for good embryos with implantation potential and euploidy.Scientific reports · 2026Article
- Stability and reliability of artificial intelligence models in embryo selection for in vitro fertilization.Fertility and sterility · 2026Article
- Clinical evaluation and predictive model development of AI-based non-invasive embryo selection inFrontiers in endocrinology · 2026Article
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- Implementation readiness of artificial intelligence inFrontiers in endocrinology · 2026Review
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- Innovations in assisted reproductive technology through the advances in artificial intelligence and image processing.Obstetrics & gynecology science · 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
- Cleavage-stage embryo segmentation using SAM-based dual branch pipeline: development and evaluation with the CleavageEmbryo dataset.Bioinformatics (Oxford, England) · 2025Article
- Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.Reproductive biology and endocrinology : RB&E · 2025Article
- Review
- Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hr post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo's implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p<0.0001) from five different fertility centers.
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What OpenQuestion holds
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.