ReviewJournal of assisted reproduction and genetics2023
New frontiers in embryo selection.
Review in Journal of assisted reproduction and genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Correlation between artificial intelligence-based IDAScore and embryo morphokinetic parameters: a retrospective cohort study.BMC pregnancy and childbirth · 2026Article
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Transcriptomics empower assisted reproductive technology in decoding gamete and embryo development potential: a review.Journal of assisted reproduction and genetics · 2025Review
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Mitochondrial FIS1 level in cumulus cells correlates with morphological grades of human cleavage-stage embryos.Journal of assisted reproduction and genetics · 2025Article
- The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges.Open medicine (Warsaw, Poland) · 2025Article
- Review
- Advanced KPI framework for IVF pregnancy prediction models in IVF protocols.Scientific reports · 2024Article
- COVID-19 and Female Fertility: An Observational Prospective Multicenter Cohort Study: Upholding Reproductive Rights in Emergency Circumstances.Diagnostics (Basel, Switzerland) · 2024Article
- Abnormal cleavage up to Day 3 does not compromise live birth and neonatal outcomes of embryos that have achieved full blastulation: a retrospective cohort study.Human reproduction (Oxford, England) · 2024Article
- A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.Journal of ovarian research · 2024Article
- Criteria for implementing artificial intelligence systems in reproductive medicine.Clinical and experimental reproductive medicine · 2024Article
- Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.
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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.