ArticleBiophysics reports2023
An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology.
Article in Biophysics reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 3 citations in OpenAlex.
- The Building and Application of a Machine Learning Model on Predicting Euploid Blastocysts in IVF Treatments Without PGT-A.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).Reproductive biology and endocrinology : RB&E · 2026Review
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- The clinical application and challenges of preimplantation genetic testing.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimplantation DNA methylation screening (PIMS) revealed that whole genome DNA methylation level is a novel biomarker for assessing ART embryo quality. Here, we reinforced and estimated the clinical efficacy of PIMS. We introduce PIMS-AI, an innovative artificial intelligence (AI) based model, to predict the probability of an embryo producing live birth and subsequently assist ART embryo selection. Our model demonstrated robust performance, achieving an area under the curve (AUC) of 0.90 in cross-validation and 0.80 in independent testing. In simulated embryo selection, PIMS-AI attained an accuracy of 81% in identifying viable embryos for patients. Notably, PIMS-AI offers significant advantages over conventional preimplantation genetic testing for aneuploidy (PGT-A), including enhanced embryo discriminability and the potential to benefit a broader patient population. In conclusion, our approach holds substantial promise for clinical application and has the potential to significantly improve the ART success rate.
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What OpenQuestion holds
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.