ArticleReproductive sciences (Thousand Oaks, Calif.)2023
Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction.
Article in Reproductive sciences (Thousand Oaks, Calif.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Metabolomic alterations in follicular fluid across female infertility conditions in IVF: a systematic review.Journal of assisted reproduction and genetics · 2026Pooled it
- Review
- Multi-omics biomarkers in female fertility: from oocyte quality to endometrial receptivity and clinical translation.Biomarker research · 2026Review
- Embryo metabolite analysis and implantation potential prediction using chemiluminescent microfluidic chips with dielectric wetting valves.Nature communications · 2026Article
- Implementation readiness of artificial intelligence inFrontiers in endocrinology · 2026Review
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- Semen HPV and IVF: insights from infection prevalence to embryologic outcomes.Journal of assisted reproduction and genetics · 2025Article
- The Integration of Artificial Intelligence inJournal of pharmacy & bioallied sciences · 2025Review
- Construction and evaluation of machine learning-based prediction model for live birth following fresh embryo transfer in IVF/ICSI patients with polycystic ovary syndrome.Journal of ovarian research · 2025Article
- Metabolomic Biomarkers in Bovine Embryo Culture Media and Their Relationship with the Developmental Potential of In Vitro-Produced Embryos.International journal of molecular sciences · 2025Article
- 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
- Comparison of the Mitophagy and Apoptosis Related Gene Expressions in Waste Embryo Culture Medium of Female Infertility Types.Life (Basel, Switzerland) · 2024Article
- Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.Reproductive sciences (Thousand Oaks, Calif.) · 2024Review
- Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.Reproductive biology and endocrinology : RB&E · 2024Observational
- The Role of One-Carbon Metabolism and Methyl Donors in Medically Assisted Reproduction: A Narrative Review of the Literature.International journal of molecular sciences · 2024Review
- The prospect of artificial intelligence to personalize assisted reproductive technology.NPJ digital medicine · 2024Review
- Associations of the Single Bovine Embryo Growth Media Metabolome with Successful Pregnancy.Metabolites · 2024Article
- Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine.International journal of molecular sciences · 2022Review
- Data-Driven Medicine in the Diagnosis and Treatment of Infertility.Journal of clinical medicine · 2022Article
- From the Understanding of Maternal Molecules and Mechanisms to Predicting Embryonic Development.Reproductive medicine and biologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100% accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.
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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.