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精细的轨迹平滑和深度学习对人类精子运动性的分类
Sahar Shahali1, Sharon T Mortimer2, Robert McLachlan3,4
1Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, Australia.
Human reproduction (Oxford, England)
|February 3, 2026
概括
精确的轨迹平滑可以改善精子运动分析. 在原始精子轨迹数据上的深度学习准确地分类渐进和过度激活的精子模式,增强计算机辅助精子分析 (CASA) 系统.
科学领域:
- 生殖生物学与人类生物学
- 医学中的人工智能
- 生物医学信号处理
背景情况:
- 传统的计算机辅助精子分析 (CASA) 系统经常使用基本的光滑算法来提取运动参数,导致关键指标的不准确性,例如节拍交叉频率 (BCF) 和横向头部位移幅度 (ALH).
- 这些不准确性可能导致精子的错误分类,特别是在识别过度激活精子时,这对于辅助生殖技术 (ART) 至关重要.
研究的目的:
- 为了研究精确的轨迹平滑是否可以增强精子运动特征的提取.
- 确定在原始精子轨迹数据上训练的深度学习模型是否可以准确地分类不同的精子运动模式,例如渐进和过度激活.
主要方法:
- 通过比较高斯过程回归 (GPR),移动平均值和离散等边形变换 (DCT) 平滑,以使用2326个精子轨迹来估计平均路径.
- 引入了一种新的度量,路径平均宽度 (PAW),以量化横向头部位移.
- 开发了一组InceptionTime深度学习模型,用于培训精子分类的原始 (x, y) 坐标序列.
主要成果:
- 使用 12 个频率组件 (DCT-12) 的离散小数点变换 (DCT) 调整产生了最一致的平均路径,改善了 BCF 和 ALH 的准确性.
- 新的PAW指标有效地区分了过度激活的 (5.5 ± 1.5μm) 和渐进的精子 (2.0 ± 1.3μm).
- InceptionTime模型在分类渐进与过度激活的轨迹方面达到89%的准确性,在运动度方面达到78%的准确性.
结论:
- 拟议的方法提高精子运动参数提取精度使用频域平滑,并通过深度学习的原始轨迹数据精子运动模式的准确分类.
- 这种综合方法解决了CASA系统的核心局限性,并显示了在ART工作流中实时应用的潜力.
- 未来的工作应侧重于使用高粘度介质数据和多中心验证的培训模型,以改善临床翻译和通用性.
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