ArticleJournal of translational medicine2025
Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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.
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.
- Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2026Article
- Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin.NPJ digital medicine · 2026Article
- Non-Invasive Extracellular Vesicle Biomarkers in Endometriosis, Molecular Signatures Linking Pelvic Inflammation, Oocyte Quality, and IVF OutcomesCurrent issues in molecular biology · 2025Review
- Correction: Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.Journal of translational medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
backgroundInfertility affects approximately 15% of couples globally, with assisted reproductive technologies (ARTs) becoming the primary interventions. Despite the growing use of ARTs, success rates have plateaued at around 30%, highlighting the need for improved predictive models to enhance outcomes. This study aimed to develop a machine learning-based predictive model for live birth outcomes following fresh embryo transfer.
methodsA total of 51,047 ART records were collected from 2016 to 2023 at the Shanghai First Maternity and Infant Hospital. After data preprocessing, 11,728 records and 55 pre-pregnancy features were analyzed. Six machine learning models-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machines (GBM), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and Artificial Neural Network (ANN)-were employed to construct the prediction model.
resultsAmong the models, RF demonstrated the best predictive performance, achieving an area under the curve (AUC) value exceeding 0.8. Key predictive features included female age, grades of transferred embryos, number of usable embryos, and endometrial thickness. A web tool was developed to assist clinicians in predicting outcomes and individualizing treatments based on patient data.
conclusionsThis study presents a significant advancement in predicting live birth outcomes prior to embryo transfer, moving beyond traditional assessments. The findings underscore the potential of machine learning to improve clinical decision-making and enhance patient counseling in ARTs.
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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.