ArticleJournal of cellular and molecular medicine2021
Application of convolutional neural network on early human embryo segmentation during in vitro fertilization.
Article in Journal of cellular and molecular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 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
- AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.Journal of assisted reproduction and genetics · 2026Review
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.Scientific reports · 2024Article
- The prospect of artificial intelligence to personalize assisted reproductive technology.NPJ digital medicine · 2024Review
- Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.Journal of assisted reproduction and genetics · 2024Review
- Recent advancements to increase success in assisted reproductive technologies in cattle.Animal reproduction · 2024Review
- Time-lapse observation of mouse preimplantation embryos using a simple closed glass capillary method.Scientific reports · 2023Article
- Article
- Variation of Female Pronucleus Reveals Oocyte or Embryo Chromosomal Copy Number Variations.Advanced genetics (Hoboken, N.J.) · 2023Article
- The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization.Sensors (Basel, Switzerland) · 2022Article
- Semantic segmentation of human oocyte images using deep neural networks.Biomedical engineering online · 2021Article
- Application of convolutional neural network on early human embryo segmentation during in vitro fertilization.Journal of cellular and molecular medicine · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Selection of the best quality embryo is the key for a faithful implantation in in vitro fertilization (IVF) practice. However, the process of evaluating numerous images captured by time-lapse imaging (TLI) system is time-consuming and some important features cannot be recognized by naked eyes. Convolutional neural network (CNN) is used in medical imaging yet in IVF. The study aims to apply CNN on day-one human embryo TLI. We first presented CNN algorithm for day-one human embryo segmentation on three distinct features: zona pellucida (ZP), cytoplasm and pronucleus (PN). We tested the CNN performance compared side-by-side with manual labelling by clinical embryologist, then measured the segmented day-one human embryo parameters and compared them with literature reported values. The precisions of segmentation were that cytoplasm over 97%, PN over 84% and ZP around 80%. For the morphometrics data of cytoplasm, ZP and PN, the results were comparable with those reported in literatures, which showed high reproducibility and consistency. The CNN system provides fast and stable analytical outcome to improve work efficiency in IVF setting. To conclude, our CNN system is potential to be applied in practice for day-one human embryo segmentation as a robust tool with high precision, reproducibility and speed.
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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.