Artificial Intelligence for Automated Semen Analysis from Microscopy to Clinical Translation - Shimaa B. bahy; Mostafa Ahmed; Ahmed A. Elngar (Journal of Smart Algorithms and Applications (JSAA), 2026)
Male-factor infertility is commonly evaluated using semen analysis; however, manual assessment of sperm concentration, motility, and morphology remains susceptible to observer variability and laboratory heterogeneity. This review synthesizes artificial intelligence methods for automated semen analysis, covering sperm detection, segmentation, morphology classification, motility assessment, tracking, and hybrid computational pipelines. A literature search was conducted across PubMed/MEDLINE, IEEE Xplore, Scopus, Web of Science Core Collection, and Google Scholar. Eligible studies were peer-reviewed, English-language investigations that applied machine-learning or deep-learning methods to at least one semen-analysis task and reported quantitative performance measures. The reviewed studies demonstrate recurring use of transfer learning, one-stage object detection, U-Net- and Mask R-CNN-based segmentation, video-level convolutional models, transformer-based architectures, model ensembles, and generative data augmentation. Although many studies report high performance on curated internal datasets, comparisons across studies are limited by differences in species, imaging modality, annotation procedures, evaluation metrics, and data-partitioning strategies. External validation, patient- or donor-level leakage control, calibration, uncertainty estimation, and prospective assessment of clinical utility remain uncommon. Hybrid systems appear most justified when their components address complementary failure modes and when theirincremental value is evaluated against an appropriately matched single-model baseline; however, such comparisons are not consistently reported. Overall, the available evidence supports the technical feasibility of artificial intelligence for automated semen analysis but does not yet establish readiness for routine clinical deployment. Translation will require harmonized multicenter datasets, consensus-based annotation, leakage-aware external validation, task-specific reporting standards, defined human oversight, and regulatory planning appropriate to the intended use and jurisdiction.