Runwei Guan, Shaofeng Liang, Jiacheng Weng, Xiaoyi Gu, Jia Weng, Daizong Liu, Duo Pan, Qingxin Zhang et al.
This is a comprehensive review paper that organizes current AI technologies for automating and enhancing the accuracy of semen analysis, a key component in male infertility diagnosis, and outlines the challenges for clinical translation.
Conventional semen analysis is labor-intensive and suffers from subjective operator-dependent variability. There is a need for objective, reproducible computational methods, and significant technical and regulatory barriers must be overcome for real-world clinical deployment.
The paper reviews task-specific deep learning methodologies for sperm detection, counting, motility assessment, and morphology classification. It summarizes public datasets, benchmarks, evaluation metrics, and multimodal strategies integrating images, videos, and clinical metadata. Finally, it proposes a staged clinical translation roadmap covering standardization, multicenter validation, and regulatory approval.
This review systematizes the progress from task-specific visual recognition to trustworthy multimodal reproductive intelligence in semen analysis. It highlights both the advancements and the unresolved challenges required to translate AI-driven analysis into clinically meaningful decision support, moving beyond mere algorithmic performance.