一篇发表在arXiv上的综述论文详细介绍了深度学习和计算机视觉技术在精液分析中诊断男性不育症的应用。该论文综合了当前AI驱动的精子检测、计数、活力评估和形态分类方法。它还讨论了临床转化中的挑战，如数据稀缺和领域转移，并提出了将这些AI工具整合到临床实践中的路线图。 AI
影响 将AI整合到精液分析中可能导致更客观、可重复的男性不育症诊断，从而改善治疗规划和辅助生殖技术。
排序理由 该集群包含一篇详细介绍AI在医学领域应用的学术论文。
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arXiv:2607.08429v1 Announce Type: cross Abstract: Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine l…
Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility stat…
Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by i…
arXiv:2607.05311v1 Announce Type: new Abstract: Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor…
Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by i…