From Manual & CASA Semen Analysis to AI: Predictive Modeling of Leucocytospermia and Antimicrobial Resistance in Male Subfertility
Suman Sharma and M. Prakash*
Research Department of Microbiology, Kanchi Shri Krishna College of Arts and Science, Kancheepuram, Tamilnadu, India
*Corresponding author
Abstract:
Male factor fertility contributes to nearly half of all cases of couple subfertility, and semen analysis remains the cornerstone of its initial evaluation. Traditional manual semen analysis, performed according to World Health Organization (WHO) criteria, is inexpensive and widely available but suffers from substantial inter- and intra-observer variability. Computer-Assisted Semen Analysis (CASA) improved objectivity and introduced quantitative kinematic parameters, yet it still requires expert quality control and cannot, by itself, resolve the clinical question of an underlying genital tract infection. Leucocytospermia — an elevated concentration of peroxidase-positive leukocytes in semen — is a common but frequently under-recognized contributor to male sub fertility, acting through oxidative stress, cytokine-mediated inflammation, and direct sperm membrane and DNA damage. Its management is increasingly complicated by rising antimicrobial resistance (AMR) among common urogenital pathogens, which delays effective treatment and prolongs exposure of sperm to an inflammatory seminal environment. This article reviews the evolution from manual to CASA-based semen analysis, summarises the pathophysiology and diagnostic approach to leucocytospermia, and outlines the growing problem of AMR in andrology. Building on this foundation, we propose and illustrate a machine-learning (ML) framework that integrates CASA kinematic data, seminal biochemical markers, clinical variables and microbiological data to (1) predict the presence and severity of leucocytospermia without relying solely on peroxidase staining, and (2) predict the likely antimicrobial susceptibility profile of the causative organism ahead of formal culture results. Representative, illustrative performance figures are presented to demonstrate how such models could be benchmarked, together with a discussion of validation requirements, explainability, and the ethical and practical barriers to clinical adoption. We conclude that AI-augmented andrology has the potential to shorten the diagnostic pathway for infection-associated subfertility and to support antimicrobial stewardship, provided that models are developed and validated prospectively, transparently, and across diverse patient populations.
Keywords:
Semen analysis; CASA; leucocytospermia; antimicrobial resistance; male infertility; machine learning; artificial intelligence; antimicrobial stewardship
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Mimansha Sahu, Maiti S. K., Ali S. L., Nidhi Rawat, Nitin Gade, Kanwar B. P. S., Prafulla Kashyap, Manish Kumar Baghel, Samidha Manikpuri, Priyanka Singh, Deeksha Sahu and Bhumika Kaushik. 2026. Recent Advances in Canine Blood Transfusion: A Review
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https://doi.org/10.20546/ijcmas.2026.sp13.054
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National Academy of Agricultural Sciences (NAAS) 
