ArticleLab on a chip2019
Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.
Article in Lab on a chip, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 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
- Ensemble deep learning with advanced feature engineering for embryo evaluation on in-vitro fertilisation procedures using biomedical images.Scientific reports · 2025Article
- Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta- analysis of artificial intelligence in embryo assessment.Contraception and reproductive medicine · 2025Review
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- The integration of artificial intelligence in assisted reproduction: a comprehensive review.Frontiers in reproductive health · 2025Review
- 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
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status.Journal of assisted reproduction and genetics · 2023Article
- Embryo selection through artificial intelligence versus embryologists: a systematic review.Human reproduction open · 2023Article
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.PloS one · 2022Article
- A Fluorescent Biosensor for Sensitive Detection ofBiosensors · 2021Article
- Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.Journal of assisted reproduction and genetics · 2021Article
- Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.Nature biomedical engineering · 2021Article
- Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality.Heliyon · 2021Article
- 3D-printed microneedles in biomedical applications.iScience · 2021Review
- Performance of a deep learning based neural network in the selection of human blastocysts for implantation.eLife · 2020Article
- A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics.Journal of reproduction & infertilityArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.
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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.