ArticleFertility and sterility2020
Consistency and objectivity of automated embryo assessments using deep neural networks.
Article in Fertility and sterility, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 112 citations in OpenAlex.
- Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.Nature medicine · 2024Trial
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning.Journal of biomedical physics & engineering · 2026Article
- Addressing multiple pregnancies in in vitro fertilization-embryo transfer: Current status, challenges, and solutions.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Moving from subjectivity to standardization? The emerging role of artificial intelligence in the embryology laboratory.F&S reports · 2026Article
- Recent advances in sex-specific reproductive microphysiological systems for the systematic evaluation of assisted reproductive technology.Materials today. Bio · 2026Review
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts.Journal of assisted reproduction and genetics · 2026Review
- Accurate machine learning model for human embryo morphokinetic stage detection.Journal of assisted reproduction and genetics · 2025Article
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- The Istanbul consensus update: a revised ESHRE/ALPHA consensus on oocyte and embryo static and dynamic morphological assessment†,‡.Human reproduction (Oxford, England) · 2025Article
- The Integration of Artificial Intelligence inJournal of pharmacy & bioallied sciences · 2025Review
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Machine learning approaches for image classification in developmental biology and clinical embryology.Development (Cambridge, England) · 2025Review
- 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
- Nature does it better: mimickingFrontiers in veterinary science · 2025Review
- Introducing artificial intelligence and sperm epigenetics in the fertility clinic: a novel foundation for diagnostics and prediction modelling.Frontiers in reproductive health · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.Scientific reports · 2024Article
- A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study.International journal of fertility & sterility · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists.
designProspective double-blind study using retrospective data.
settingU.S.-based large academic fertility center. PATIENTS: Not applicable. INTERVENTION(S): Embryo images (748 recorded at 70 hours postinsemination [hpi]) and 742 at 113 hpi) were used to evaluate embryologists and neural networks in embryo grading. The performance of 10 embryologists and a neural network were also evaluated in disposition decision making using 56 embryos.
main outcome measuresCoefficients of variation (%CV) and measures of consistencies were compared.
resultsEmbryologists exhibited a high degree of variability (%CV averages: 82.84% for 70 hpi and 44.98% for 113 hpi) in grading embryo. When selecting blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68%, respectively. The neural network outperformed the embryologists in selecting blastocysts for biopsy and cryopreservation with a consistency of 83.92%. Cronbach's α analysis revealed an α coefficient of 0.60 for the embryologists and 1.00 for the network.
conclusionsThe results of our study show a high degree of interembryologist and intraembryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading. This may ultimately lead to less precise disposition decisions and discarding of viable embryos. The application of a deep neural network, as shown in our study, can introduce improved reliability and high consistency during the process of embryo selection and disposition, potentially improving outcomes in an embryology laboratory.
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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.