ArticleJournal of assisted reproduction and genetics2021
Embryo selection with artificial intelligence: how to evaluate and compare methods?
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. It is linked to trial NCT07611448 (An Observational Study of Artificial Intelligence-Assisted Blastocyst Morphokinetic Analysis Using Short-Duration Video and Its Association With Pregnancy Outcomes), which is not on this map. Cited by 50 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.
An Observational Study of Artificial Intelligence-Assisted Blastocyst Morphokinetic Analysis Using Short-Duration Video and Its Association With Pregnancy Outcomes
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of Endometrial Receptivity Analysis on Pregnancy Outcomes In Patients Undergoing Embryo Transfer: A Systematic Review and Meta-Analysis.Journal of assisted reproduction and genetics · 2023Pooled it
- 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
- Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- Embryologist experience affects concordance with an artificial intelligence embryo ranking algorithm: benefit of artificial intelligence assistance.F&S reports · 2026Article
- A machine learning-assisted approach to explain key determinants for achieving live birth in autologous ICSI cycles.Journal of assisted reproduction and genetics · 2026Article
- Evaluating the concordance between AI-based and conventional embryo selection: implications for clinical decision-making.Reproductive biomedicine online · 2026Article
- A hybrid model to study the demographic profile of women in view of assisted reproductive techniques through machine learning models.Frontiers in reproductive health · 2026Article
- Development and validation of a nomogram integrating endometrial ultrasound parameters to predict clinical pregnancy in frozen-thawed embryo transfer cycles.Frontiers in endocrinology · 2026Article
- 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
- Relationships of phototextural characteristics of ovine presumptive zygotes to their developmental potential in vitro†.Biology of reproduction · 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
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Predictive model for live birth outcomes in single euploid frozen embryo transfers: a comparative analysis of logistic regression and machine learning approaches.Journal of assisted reproduction and genetics · 2025Article
- Deep learning classification integrating embryo images with associated clinical information from ART cycles.Scientific reports · 2025Article
- The Integration of Artificial Intelligence inJournal of pharmacy & bioallied sciences · 2025Review
- Artificial Intelligence in Assisted Reproductive Technology: A New Era in Fertility Treatment.Cureus · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
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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.