ArticleJournal of assisted reproduction and genetics2021
Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm.
Article in Journal of assisted reproduction and genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- Strategic Guidelines to Integrate Artificial Intelligence in Obstetrics and Gynecology: Best Practices and Ethical Considerations.Reproductive sciences (Thousand Oaks, Calif.) · 2025Guideline
- Embryologist experience affects concordance with an artificial intelligence embryo ranking algorithm: benefit of artificial intelligence assistance.F&S reports · 2026Article
- Microfluidics for in vitro fertilization: from science to clinical validation.Human reproduction update · 2026Review
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Federated task-adaptive learning for personalized selection of human IVF-derived embryos.Communications medicine · 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 perception of artificial intelligence and infertility care among patients undergoing fertility treatment.Journal of assisted reproduction and genetics · 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
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 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
- 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
- Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.Journal of assisted reproduction and genetics · 2024Article
- Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.Nature communications · 2024Article
- Criteria for implementing artificial intelligence systems in reproductive medicine.Clinical and experimental reproductive medicine · 2024Article
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
- New frontiers in embryo selection.Journal of assisted reproduction and genetics · 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
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeA deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND
methodsA non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI.
resultsFourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior).
conclusionsThe incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
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