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基于深度学习的方法用于精子形态分析

Bianping Liang¹, Mingxue Wang²

¹Department of Urology, the People's Hospital of Nanchuan Chongqing, No.16 South Street, Nanchuan District, Chongqing, 408400, China. 978840595@qq.com.

|October 22, 2025

概括

人工智能和机器学习显著提升了精子形态分析以诊断男性不孕症. 深度学习模型在细分和分类精子结构方面表现有前途,提高了诊断准确度.

科学领域:

生殖生物学生殖生物学
医学成像分析医学成像分析
人工智能在医学中的应用

背景情况:

男性不孕症影响着全球人口的很大一部分.
精子形态分析 (SMA) 对于评估男性不孕症至关重要.
目前的SMA方法在准确性和效率方面存在局限性.

研究的目的:

审查SMA中机器学习 (ML) 和深度学习 (DL) 的应用.
探索DL在细分和分类完整的精子结构方面的潜力.
提供建议,以提高SMA使用AI的性能.

主要方法:

关于SMA中AI/ML研究的叙事文献综述.
对精子形态学的传统ML和DL模型的分析.
探索基于DL的细分和分类技术.

主要成果:

传统的ML和DL模型都在SMA中提供了优势和局限性.
深度学习显示了精子结构详细分析的潜力.
人工智能集成可能会提高SMA的表现.

结论:

人工智能,特别是DL,是通过高级SMA改善男性不孕症诊断的有希望的途径.
需要对DL算法进行进一步的研究,以进行完整的精子结构分析.
实施DL可以提高精子形态评估的实际性能和准确性.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.男人不孕不育的情况精子形态分析分析精子形态分析.

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