ArticleNature communications2025
Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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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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
5 citing papers in PubMed.
- Implementation readiness of artificial intelligence inFrontiers in endocrinology · 2026Review
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience.Journal of clinical medicine · 2024Article
- Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.Seminars in reproductive medicine · 2024Review
- From the Understanding of Maternal Molecules and Mechanisms to Predicting Embryonic Development.Reproductive medicine and biologyReview
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) models and the multicenter, US national registry-based model produced by Society for Assisted Reproductive Technology (SART). In a retrospective model validation study, we tested whether MLCS performs better than SART using 4635 patients' first-IVF cycle data from 6 centers. MLCS significantly improved minimization of false positives and negatives overall (precision recall area-under-the-curve) and at the 50% LBP threshold (F1 score) compared to SART (p < 0.05). To contextualize, MLCS more appropriately assigned 23% and 11% of all patients to LBP ≥ 50% and LBP ≥ 75% whereas SART gave lower LBPs. Here, we show MLCS improves model metrics relevant for clinical utility - personalizing prognostic counseling and cost-success transparency - and is externally validated. We recommend evaluating MLCS in a larger sample of fertility centers.
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Registered trials
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