ArticleNature communications2024
Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 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
- NICE: A Two-Step Non-Invasive Framework for Embryo cfDNA Read Enrichment and Quality Assessment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Technological Advances in Molecular Diagnostic Methods for Hereditary Diseases in Preconception and Prenatal Settings.Current issues in molecular biology · 2026Review
- Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection.NPJ digital medicine · 2026Article
- Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts.Journal of assisted reproduction and genetics · 2026Review
- AI prediction models based on time-lapse imaging for good embryos with implantation potential and euploidy.Scientific reports · 2026Article
- Multimodal intelligent prediction model for in vitro fertilization.NPJ digital medicine · 2026Article
- Establishment and application of a prediction model for blastocyst formation rate and delivery outcome in human assisted reproductive technology.Reproduction & fertility · 2026Article
- 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
- deepBlastoid: a deep learning model for automated and efficient evaluation of human blastoids.Life medicine · 2025Article
- Production and Use of Customizable Agarose Molds for Scaffold-Free Mouse Ovarian Follicle Culture.Journal of visualized experiments : JoVE · 2025Article
- Time-Lapse Imaging in IVF: Bridging the Gap Between Promises and Clinical Realities.International journal of molecular sciences · 2025Review
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- A foundational model for in vitro fertilization trained on 18 million time-lapse images.Nature communications · 2025Article
- Noninvasive Biomarkers of Human Embryo Developmental Potential.International journal of molecular sciences · 2025Review
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.Nature communications · 2025Article
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
Corrections and comments
- Update of
Authors and funding
15 authors.
Funding
Abstract
Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.
Indexed as
Identifiers
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.