ArticleAging2021
Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed, 22 citations in OpenAlex.
- AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.Journal of assisted reproduction and genetics · 2026Review
- 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 Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Exploration of the mechanism and therapy of ovarian aging by targeting cellular senescence.Life medicine · 2025Review
- A retrospective study: establishment and validation of the prediction model for the gonadotropin starting dose in IVF/ICSI-ET among normal ovarian response women.Frontiers in endocrinology · 2025Article
- Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.PLoS computational biology · 2023Article
- Unveiling the role of regulatory T cells in the tumor microenvironment of pancreatic cancer through single-cell transcriptomics andFrontiers in immunology · 2023Article
- Article
- GADD45B regulates the carcinogenesis process of chronic atrophic gastritis and the metabolic pathways of gastric cancer.Frontiers in endocrinology · 2023Article
- Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature.Frontiers in immunology · 2023Article
- Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma.Frontiers in molecular biosciences · 2023Article
- Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation.BMC pregnancy and childbirth · 2022Article
- Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features.Diagnostics (Basel, Switzerland) · 2022Article
- Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical complexity. Therefore, this study conducted extensive selection using models that were derived from a variety of machine learning algorithms, including random forest (RF), decision trees, eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and artificial neural networks (ANN) for the development of two models called the COS pre-launch model (CPLM) and the hCG pre-trigger model (HPTM) to assess POR based on different requirements. The results demonstrated that CPLM constructed using ANN achieved the highest AUC result of all the algorithms in COS pre-launch (AUC=0.859, C-index=0.87, good calibration), and HPTL constructed using random forest was found to be the most effective in hCG pre-trigger (AUC=0.903, C-index=0.90, good calibration). It is notable that CPLM and HPTM exhibited better performance than common clinical characteristics (0.895 [CPLM], and 0.903 [HPTM] in comparison to 0.824 [anti-Müllerian hormone (AMH)], and 0.799 [antral follicle count (AFC)]). Furthermore, variable importance figure elucidated the values of AMH, AFC, and 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.