ArticleDiagnostics (Basel, Switzerland)2022
Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 24 citations in OpenAlex.
- Enhanced predictive performance of artificial intelligence in individualized ovarian stimulation of in vitro fertilization: a retrospective cohort study.BMC medicine · 2026Article
- Associations between daytime napping and parameters of ovarian reserve among women undergoing assisted reproductive technology.BMC women's health · 2026Article
- Interpretable machine learning model for predicting MII oocyte retrieved following controlled ovarian stimulation: a retrospective cohort study of 24,976 IVF/ICSI cycles.Journal of assisted reproduction and genetics · 2025Article
- Pharmacogenetic analysis using artificial intelligence (AI) to identify polymorphisms associated with sub-optimal ovarian response and hyper-response.Journal of assisted reproduction and genetics · 2025Observational
- The Integration of Artificial Intelligence inJournal of pharmacy & bioallied sciences · 2025Review
- Clinical data-based modeling of IVF live birth outcome and its application.Reproductive biology and endocrinology : RB&E · 2024Article
- Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.Journal of medical Internet research · 2024Article
- Analysis of the potential regulatory mechanisms of female and latent genital tuberculosis affecting ovarian reserve function using untargeted metabolomics.Scientific reports · 2024Article
- Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.PLoS computational biology · 2023Article
- Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation.BMC pregnancy and childbirth · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulation strategy. Nowadays, most machine learning-based tailoring strategies aim to generally classify the controlled ovarian stimulation outcome, lacking the capacity to precisely predict the outcome and evaluate the impact features. Based on a clinical cohort composed of 1365 women and two machine learning methods of artificial neural network and supporting vector regression, a regression prediction model of the number of oocytes retrieved is trained, validated, and selected. Given the proposed model, an index called the normalized mean impact value is defined and calculated to reflect the importance of each impact feature. The proposed models can estimate the number of oocytes retrieved with high precision, with the regression coefficient being 0.882% and 89.84% of the instances having the prediction number ≤ 5. Among the impact features, the antral follicle count has the highest importance, followed by the E
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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.