ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Automated Measurements of Key Morphological Features of Human Embryos for IVF.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- Effect of cytoplasmic fragmentation on embryo development, quality, and pregnancy outcome: a systematic review of the literature.Reproductive biology and endocrinology : RB&E · 2024Pooled it
- Supervised contrastive learning for cell stage classification of animal embryos.Scientific reports · 2026Article
- Clocks and Dominoes: Timing Mechanisms of Embryogenesis.bioRxiv : the preprint server for biology · 2026Article
- An intra- and inter-class context and consistency network for supervised and semi-supervised blastocyst segmentation.Scientific reports · 2025Article
- A foundational model for in vitro fertilization trained on 18 million time-lapse images.Nature communications · 2025Article
- A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.Scientific reports · 2024Article
- A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.Scientific reports · 2024Article
- Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.Reproductive biology and endocrinology : RB&E · 2024Article
- A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning.Patterns (New York, N.Y.) · 2024Article
- BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.Human reproduction (Oxford, England) · 2024Article
- Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.Journal of assisted reproduction and genetics · 2024Review
- Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation.Journal of assisted reproduction and genetics · 2023Article
- Noninvasive metabolic profiling of cumulus cells, oocytes, and embryos via fluorescence lifetime imaging microscopy: a mini-review.Human reproduction (Oxford, England) · 2023Review
- Stain-free detection of embryo polarization using deep learning.Scientific reports · 2022Article
- Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF.Journal of assisted reproduction and genetics · 2021Review
- Interpretable, not black-box, artificial intelligence should be used for embryo selection.Human reproduction open · 2021Article
- Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and subjective. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs). Our pipeline consists of (1) semantic segmentation of the regions of the embryo, (2) regression predictions of fragment severity, (3) classification of the developmental stage, and object instance segmentation of (4) cells and (5) pronuclei. Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.
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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.