ArticleF&S reports2025
A machine learning approach using semen parameters and sperm mitochondrial DNA copy number to predict couples' fecundity.
Article in F&S reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Sperm mitochondrial DNA copy number: a marker of male fertility and reproductive success.Human reproduction update · 2026Review
- Circadian Disruption Is Associated with Elevated Whole-Semen mtDNA Copy Number and ImplicatesInternational journal of molecular sciences · 2026Article
- Comparative machine learning analysis identifies random forest and adaboost as superior models for the evaluation of semen quality and reproductive hormones.Frontiers in reproductive health · 2026Article
- Toward predictive andrology: integrating basic semen analysis mitochondrial deoxyribonucleic acid and artificial intelligence to forecast reproductive outcomes.F&S reports · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Objective: To examine the utility of semen parameters and sperm mitochondrial DNA copy number (mtDNAcn) to predict couples' time to pregnancy (TTP). Design: This study assessed the predictive power of sperm mtDNAcn and 34 semen parameters. Two composite semen quality indices (SQIs) were developed; an unweighted ranked-sperm quality index (ranked-SQI) derived from only semen parameters and a weighted sperm quality index generated using machine learning via elastic net (ElNet-SQI). Discrete-time proportional hazard models, logistic regression, and receiver operating characteristic (ROC) analyses were used to evaluate the predictive ability of achieving pregnancy at 3, 6, and 12 months, and the overall TTP. Subjects: The participants included 281 men from the Longitudinal Investigation of Fertility and the Environment study, a large preconception general population cohort designed to explore factors affecting conception. Exposure: Sperm mtDNAcn, 34 semen parameters, unweighted ranked-SQI, and a machine learning-based weighted SQI were evaluated for the ability to predict pregnancy. Main Outcomes Measures: The main outcome measures were the overall time taken to achieve pregnancy and the likelihood of achieving pregnancy within 3, 6, or 12 months of trying to conceive. Results: For individual semen measures, sperm mtDNAcn was most predictive of pregnancy at 12 menstrual cycles in ROC analyses (area under the curve [AUC], 0.68; 95% confidence interval [CI], 0.58-0.78). Among multiparameter biomarkers, ElNet-SQI (comprised of 8 semen parameters and mtDNAcn), demonstrated the highest AUC, 0.73; 95% CI, 0.61-0.84) for pregnancy status at 12 cycles. Furthermore, ElNet-SQI was the most strongly associated with TTP than any other individual or combinations of semen parameters (fecundability odds ratio [FOR], 1.30; 95% CI, 1.14-1.45; Conclusion: Sperm mtDNAcn is associated with multiple conventional and detailed semen parameters. Moreover, a composite machine learning ElNet-SQI that included mtDNAcn and several semen parameters had the highest predictive ability of pregnancy. These results indicate that sperm mtDNAcn can serve as a biomarker of overall sperm fitness and likelihood of reproductive success.
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