ReviewReproductive medicine and biology
Reassessing Semen Analysis: Clinical Insights Beyond Sperm Count and Motility.
Review in Reproductive medicine and biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Semen analysis (SA), recognized by the World Health Organization (WHO) as the cornerstone of male infertility evaluation, remains indispensable in reproductive medicine. However, advances in assisted reproductive technology (ART) and artificial intelligence (AI) have highlighted the limitations of relying solely on conventional semen parameters. Objective: To critically review the evolving clinical role of SA by integrating conventional assessment with emerging functional, molecular, and computational approaches that improve diagnostic accuracy and individualized patient care. Methods: A narrative review of contemporary evidence was conducted, focusing on conventional semen parameters, biofunctional sperm testing, omics technologies, AI-assisted analysis, and broader clinical applications of SA. Results: Conventional parameters, including sperm concentration, motility, and morphology, remain essential but inadequately reflect fertilizing capacity. Adjunctive assessments, including oxidative stress biomarkers and sperm DNA fragmentation, provide valuable insights into sperm function and reproductive potential. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, deepen mechanistic understanding, while AI enhances diagnostic precision, reproducibility, and standardization. Beyond infertility evaluation, SA also supports male contraceptive assessment, natural conception, ART, and patient counseling. Conclusions: Integrating conventional SA with functional, molecular, and AI-driven diagnostics provides a comprehensive framework for evaluating male fertility, advancing precision reproductive medicine and personalized clinical management.
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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.