ArticleAJOG global reports2023
Embryo ploidy status classification through computer-assisted morphology assessment.
Article in AJOG global reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06762704 (A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
A Non-Invasive Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy: a Multi-Center, Prospective, Non-Randomized Validation Study
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- "Old and new" frontiers in embryo viability assessment: current practices and introduction of emerging approaches.Biology of reproduction · 2026Review
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Noninvasive Biomarkers of Human Embryo Developmental Potential.International journal of molecular sciences · 2025Review
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study.Reproductive biology and endocrinology : RB&E · 2024Article
- Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images.Journal of reproduction & infertilityArticle
- Advanced Embryo Ploidy Classification Using Vision Transformers: Integration of Sequential Time-Lapse Imaging and Undersampling Techniques: A Retrospective Study.Journal of human reproductive sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy.
objectiveComputer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment. STUDY
designImage processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm.
resultsAn efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84.
conclusionThis research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.
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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.