ArticleScientific reports2023
An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- FertilitY Predictor-a machine learning-based web tool for the prediction of assisted reproduction outcomes in men with Y chromosome microdeletions.Journal of assisted reproduction and genetics · 2025Pooled it
- Explainable AI consensus for grading human blastocysts and blastoids.Reproduction & fertility · 2026Article
- 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
- Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images.Medicina (Kaunas, Lithuania) · 2026Article
- 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 potential of combined secretomics and image-based morphometry as a non-invasive method for selecting implanting embryos.Reproductive biology and endocrinology : RB&E · 2025Article
- A review of artificial intelligence applications in in vitro fertilization.Journal of assisted reproduction and genetics · 2025Review
- Ensemble learning for fetal ultrasound and maternal-fetal data to predict mode of delivery after labor induction.Scientific reports · 2024Article
- Predicting implantation by using dual AI system incorporating three-dimensional blastocyst image and conventional embryo evaluation parameters-A pilot study.Reproductive medicine and biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated with implantation potential. Multivariable regression analysis, adjusted for woman age, found blastocyst size to be significantly associated with implantation potential. The odds of implantation increased by 1.74 for embryos with a blastocyst size greater than the mean (147 ± 19.1 μm). The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a precise, consistent, and time-saving tool for improving blastocyst selection.
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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.