ReviewActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2019
A Review of Machine Learning Approaches in Assisted Reproductive Technologies.
Review in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.Journal of clinical medicine · 2026Review
- SEED-ML: A multi-parametric clinical dataset on male infertility for predictive modeling and AI research.Data in brief · 2026Article
- [Correlational research on body fat rate and skeletal muscle rate with depression in the population of assisted reproduction].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Article
- Applications of artificial intelligence in bovine reproductive assessment: focus on oocytes and blastocysts.Journal of assisted reproduction and genetics · 2026Review
- Prediction of in-vitro fertilization outcome by ultrasound strain analysis and machine learning: A multi-center study.The ultrasound journal · 2026Article
- Psychological depression and its association with oocyte yield and embryo outcomes in infertile women undergoingFrontiers in psychiatry · 2026Article
- Enhancing frozen-thawed embryo transfer outcomes and treatment personalization through machine learning models.Journal of assisted reproduction and genetics · 2025Article
- Semen HPV and IVF: insights from infection prevalence to embryologic outcomes.Journal of assisted reproduction and genetics · 2025Article
- Machine Learning Methods to Characterize Mouse and Human Oocytes.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.Nature communications · 2024Article
- Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.Reproductive biology and endocrinology : RB&E · 2024Observational
- A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.Journal of ovarian research · 2024Article
- A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization/intracytoplasmic sperm injection treatment: A cross-sectional study.International journal of reproductive biomedicine · 2023Article
- Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Artificial Intelligence in Reproductive Medicine - An Ethical Perspective.Geburtshilfe und Frauenheilkunde · 2023Article
- Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes afterFrontiers in endocrinology · 2023Article
- Multi-omics and machine learning for the prevention and management of female reproductive health.Frontiers in endocrinology · 2023Review
- Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles.AJOG global reports · 2022Article
- Prevalence and associated risk factors for anxiety and depression in infertile couples of ART treatment: a cross-sectional study.BMC psychiatry · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAssisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART's makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle.
aimThis review provides an overview on machine learning-based prediction models in ART.
methodsThis article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar.
resultsWe identified 20 papers reporting on machine learning-based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies.
conclusionMachine learning-based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.
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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.