ArticleJournal of visualized experiments : JoVE2018
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes.
Article in Journal of visualized experiments : JoVE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social Freezing: Pressing Pause on Fertility.International journal of environmental research and public health · 2021Pooled it
- Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle.Scientific reports · 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
- Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.Communications biology · 2025Article
- Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review.Bioengineering & translational medicine · 2024Review
- Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine.Journal of assisted reproduction and genetics · 2023Review
- Artificial Intelligence in Reproductive Medicine - An Ethical Perspective.Geburtshilfe und Frauenheilkunde · 2023Article
- Semantic segmentation of human oocyte images using deep neural networks.Biomedical engineering online · 2021Article
- Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.Frontiers in immunology · 2021Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
- Artificial intelligence in reproductive medicine.Reproduction (Cambridge, England) · 2019Review
- TheJournal of reproduction & infertilityArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure. Oocytes are isolated from fully-grown antral follicles and cultured for 15 h inside a microscope equipped for time-lapse analysis at 37 °C and 5% CO2. Pictures are taken at 8 min intervals. The images are analyzed using the Particle Image Velocimetry (PIV) method that calculates, for each oocyte, the profile of Cytoplasmic Movement Velocities (CMVs) occurring throughout the culture period. Finally, the CMVs of each single oocyte are fed through a mathematical classification tool (Feed-forward Artificial Neural Network, FANN), which predicts the probability of a gamete to be developmentally competent or incompetent with an accuracy of 91.03%. This protocol, set up for the mouse, could now be tested on oocytes of other species, including humans.
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Registered trials
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