Artificial Intelligence in Unexplained Infertility: A Systematic Review of Machine Learning-Based Predictive Models.
Introduction: This systematic review evaluates the methodological rigor and risk of bias of machine learning-based predictive models developed to estimate the success of assisted reproductive technologies in cases of unexplained infertility, using the Prediction Model Risk of Bias Assessment Tool. M...
| Published in: | Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 290 - 305 |
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| Main Authors: | , |
| Format: | research systematic review tables/charts Journal Article |
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KARE Publishing
Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27917835 N1MV jtl: Lokman Hekim Health Sciences issn: 27917835 maglogo: N pubinfo: dt: Jun2026 vid: 6 iid: 2 pid: 62027 pub: KARE Publishing artinfo: ui: 194639955 194639955 194639955 10.14744/lhhs.2026.91780 194639955 ppf: 290 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence in Unexplained Infertility: A Systematic Review of Machine Learning-Based Predictive Models. aug: au: Tahta, Tugba Afir, Özge Aydogan affil: Department of Midwifery, Ankara Medipol University Faculty of Health Sciences, Ankara, Türkiye sug: subj: Infertility Diagnosis Artificial Intelligence Reproduction Techniques Machine Learning Prediction Models Human Systematic Review Embase PubMed Cochrane Library Descriptive Statistics Data Analysis, Statistical Treatment Outcomes Risk Assessment Checklists Research Methodology Predictive Value of Tests ab: Introduction: This systematic review evaluates the methodological rigor and risk of bias of machine learning-based predictive models developed to estimate the success of assisted reproductive technologies in cases of unexplained infertility, using the Prediction Model Risk of Bias Assessment Tool. Methods: A systematic review was conducted to assess predictive modeling studies focused on unexplained infertility and based on machine learning, guided by the framework of the Prediction Model Risk of Bias Assessment Tool. After rigorously screening 912 records, only three studies met the inclusion criteria. While limited in number, these studies highlight emerging evidence in this underexplored area. Results: The included studies applied supervised machine learning algorithms, such as Random Forest, Support Vector Machines, Partial Least Squares Discriminant Analysis, and neural networks, across various biomedical data types. Reported predictive performance varied by data modality: spectroscopy-based models demonstrated high classification accuracy, ranging from 92% to 100%, while a couple-based metabolic model with external validation achieved an accuracy of 73.8%. According to the PROBAST assessment, two studies were rated as low risk of bias, whereas one study exhibited an unclear risk, primarily due to limitations in external validation and analytical transparency. Discussion and Conclusion: This systematic review demonstrates the potential of machine learning-based models to enhance clinical decision-making in the context of unexplained infertility. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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