ArticleJournal of assisted reproduction and genetics2024
Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.
Article in Journal of assisted reproduction and genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Smartphone-Assisted CSCTAS-Derived Sperm Kinematics and Clinical Pregnancy after Intrauterine Insemination in a Turkish Cohort of Couples with Unexplained Infertility.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Comparison of Clinical Characteristics and Pregnancy Outcome Prediction Models Between AIH and AID Patients.International journal of women's health · 2026Article
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- Effect of air pollution on clinical pregnancy outcomes following intrauterine insemination: a machine learning-based analysis.Frontiers in public health · 2026Article
- Development and evaluation of clinical pregnancy prediction models for intrauterine insemination using three machine learning algorithms.Frontiers in endocrinology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population.
methods
resultsA total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy.
conclusionsThe machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.
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Registered trials
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