ArticleProceedings of the National Academy of Sciences of the United States of America2010
Deep phenotyping to predict live birth outcomes in in vitro fertilization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed.
- Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.Nature communications · 2025Article
- 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
- Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model.Scientific reports · 2021Article
- Embryos derived from donor or patient oocytes are not different for in vitro fertilization outcomes when PGT allows euploid embryo selection: a retrospective study.Clinical and translational medicine · 2020Article
- Article
- An artificial neural network for the prediction of assisted reproduction outcome.Journal of assisted reproduction and genetics · 2019Article
- Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.Journal of assisted reproduction and genetics · 2018Review
- Promoting the use of elective single embryo transfer in clinical practice.Fertility research and practice · 2016Review
- Article
- External validation and calibration of IVFpredict: a national prospective cohort study of 130,960 in vitro fertilisation cycles.PloS one · 2015Article
- Defining a comprehensive verotype using electronic health records for personalized medicine.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Derivation of human embryonic stem cells using a post-inner cell mass intermediate.Nature protocols · 2013Article
- Boosting predictions of treatment success.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
- Prognostic factors associated with clinical pregnancy in in vitro fertilization using pituitary down-regulation with depot and daily low-dose luteal phase gonadotropin releasing hormone agonists: A single center's experience.Journal of human reproductive sciencesArticle
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Authors and funding
9 authors.
Funding
Abstract
Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVFBT, by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003-2006, followed by external validation with 634 cycles from 2007-2008, respectively. We tested whether this model could predict the probability of having a live birth in the subsequent treatment (C2). By using nondeterministic methods to identify prognostic factors and their relative nonredundant contribution, we generated a prediction model, IVF(BT), that was superior to the age-based control by providing over 1,000-fold improvement to fit new data (p<0.05), and increased discrimination by receiver-operative characteristic analysis (area-under-the-curve, 0.80 vs. 0.68 for C1, 0.68 vs. 0.58 for C2). IVFBT provided predictions that were more accurate for approximately 83% of C1 and approximately 60% of C2 cycles that were out of the range predicted by age. Over half of those patients were reclassified to have higher live birth probabilities. We showed that data from a prior cycle could be used effectively to provide personalized and validated live birth probabilities in a subsequent cycle. Our approach may be replicated and further validated in other IVF clinics.
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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.