基础科学和病原发生学
Jerry Jierui Lou1, Peter Chang1, Kiana D Nava2
1University of California, Irvine, School of Medicine, Irvine, CA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
本研究引入了一种机器学习 (ML) 算法,用于自动化大脑动脉样硬化评估,比手工方法提高准确性和效率. 艺术和网站工具提供了一个有前途的方法,以完整的幻灯片图像来增强血管病理的分析.
科学领域:
- 神经病理学神经病理学
- 计算病理学计算病理学
- 医学成像分析 医学成像分析
背景情况:
- 目前的大脑动脉样硬化症的评估依赖于手动组织学检查的主观,半定量尺度.
- 手动方法表现出有限的评价者间可靠性,缺乏精确的定量指标.
- 区域特定的高分辨率分析通常是手动不切实际的.
研究的目的:
- 开发和验证基于机器学习 (ML) 的算法,用于动脉缩性血管的自动形态测量分析.
- 引入动脉样硬化细分 (ArtS) 和硬化指数和厚度提取器 (SITE) 用于对整个幻灯片图像 (WSI) 的定量评估.
主要方法:
- 来自三个大脑银行的数字化血素和素染色大脑WSIs.
- 在ArtS中训练了三个ML模型,用于血管检测,动脉样硬化分类和血管壁/光层细分.
- 利用SITE工具从细分的血管中提取硬化指数和壁厚.
主要成果:
- 在血管检测 (AUC-ROC高达0.79) 和动脉样硬化症分类 (精度高达0.94) 方面,ArtS表现强.
- 动脉样硬化血管细分实现了达斯得分高达0.73和AUC-ROC高达0.92.
- 网站成功计算了与专家评估可比的定量指标.
结论:
- ArtS和SITE算法显示了对动脉样硬化症的定量形态测量分析的巨大潜力.
- 这些ML工具可以增强目前针对血管病理的基于人类的评估方法.
- 进一步优化可能会导致神经病理学家有价值的辅助工具.
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