Rapid technologies for animal semen quality assessment, reproductive disease diagnosis, and sperm preservation: A review.

Accurate assessment of animal semen quality is essential for artificial insemination efficiency, genetic resource conservation, and sustainable development of animal husbandry. Although conventional semen evaluation methods (e.g., microscopic morphological observation, flow cytometry, and hypo-osmotic swelling test) have been standardized, they generally suffer from long processing times, dependence on laboratory equipment, and the need for professional operation, making them inadequate for rapid-response scenarios such as on-farm breeding guidance and real-time semen viability monitoring. This review provides a critical, systematic synthesis of the latest advances from 2020 to 2026 in rapid animal semen quality detection technologies, with particular focus on field validation status and practical implementation feasibility. It focuses on comparing two branches of computer-assisted sperm analysis (CASA) traditional image-based processing systems and emerging deep learning approaches in terms of analytical sensitivity, portability, and field-deployability. Furthermore, this article reports on the application of microfluidic chip technology and smartphone-based imaging systems as next-generation point-of-care tools, enabling multiparameter analysis at the single-sperm level. The potential of portable biosensors (e.g., impedance sensors and lateral flow strips) for rapid detection of oxidative stress and DNA fragmentation in semen is also reviewed, along with the growing role of artificial intelligence in automated sperm morphology classification and motility trajectory prediction. Building on a unified biological framework linking molecular targets to fertility outcomes, this review systematically evaluates emerging fertility-associated biomarkers including proteomic signatures, extracellular vesicles, non-coding RNAs, and epigenetic markers, and critically assesses their predictive value for field reproductive performance. A modified QUADAS-2 framework tailored for veterinary diagnostics is applied to grade the strength of evidence for all included technologies, distinguishing high-quality multi-center validated methods from proof-of-concept prototypes, and systematically identifying sources of bias such as spectrum bias, operator blinding deficits, and developer-led validation studies. Quantitative analysis shows that only 7.1% of included studies meet high-quality evidence standards, while 82.5% of emerging technology evaluations carry performance overestimation bias due to developer-led trial design. A three-tiered technology maturity classification is established to distinguish established laboratory standards, field-validated developing tools, and proof-of-concept emerging technologies, clarifying the practical readiness of each method. Advanced rapid detection methods have reduced total assay time from 30 to 60 min (conventional manual microscopy) to 5-15 min; however, challenges remain regarding standardization of sample pretreatment and interference caused by semen heterogeneity. A structured, scenario-based technology selection framework with fully quantitative comparisons of capital equipment cost, per-test reagent cost, training investment, and cost-effectiveness across deployment scales, with benchmarking against conventional manual microscopy and laboratory gold-standard methods to clarify incremental cost-effectiveness ratios for each deployment scenario, is proposed to assist researchers and practitioners in selecting appropriate detection methods according to specific needs, including sensitivity, cost, throughput, and deployment scenarios. A multidimensional comparative analysis across 12 standardized performance criteria demonstrates that deep learning-assisted imaging based on microfluidic chips achieves the best balance among detection speed, accuracy, and field deployability, whereas flow cytometry remains the quantitative gold standard for laboratory semen quality assessment. This review also provides an in-depth analysis of current standardization gaps and regulatory frameworks for veterinary semen diagnostic devices across major global jurisdictions, identifying key barriers to commercial translation including lack of certified reference materials, underdeveloped inter-laboratory proficiency testing schemes, and fragmented regulatory pathways for point-of-care products. Finally, a phased 10-year translational roadmap is proposed, highlighting priority directions including multi-omics integration, multiplex point-of-care platform development, cross-species diagnostic standardization, and commercial translation of laboratory prototypes.