High-throughput digital pathology offers considerable advantages over traditional semiquantitative and manual methods of counting pathology. We used brain tissue from 5 clinical-pathologic cohort studies of aging; the Religious Orders Study, the Rush Memory and Aging Project, the Minority Aging Research Study, the African American Clinical Core, and the Latino Core to (1) develop a workflow management system for digital pathology processes, (2) optimize digital algorithms to quantify Alzheimer disease (AD) pathology, and (3) harmonize data statistically. Data from digital algorithms for the quantification of β-amyloid (Aβ, n = 413) whole slide images and tau-tangles (n = 639) were highly correlated with manual pathology data (r = 0.83 to 0.94). Measures were robust and reproducible across different magnifications and repeated scans. Digital measures for Aβ and tau-tangles across multiple brain regions reproduced established patterns of correlations, even when samples were stratified by clinical diagnosis. Finally, we harmonized newly generated digital measures with historical measures across multiple large autopsy-based studies. We describe a multidisciplinary approach to develop a digital pathology pipeline that reproducibly identifies AD neuropathologies, Aβ load, and tau-tangles. Digital pathology is a powerful tool that can overcome critical challenges associated with traditional microscopy methods.

中文翻译：

---

大型尸检研究中阿尔茨海默病病理学和相关基础设施的高通量数字量化。

与传统的半定量和手动计数病理学方法相比，高通量数字病理学具有相当大的优势。我们使用了来自 5 项衰老临床病理队列研究的脑组织；宗教秩序研究、匆忙记忆与衰老项目、少数族裔衰老研究、非裔美国人临床核心和拉丁裔核心，以 (1) 开发数字病理过程的工作流程管理系统，(2) 优化数字算法以量化阿尔茨海默病 (AD) 病理学，(3) 统计数据一致。用于量化 β-淀粉样蛋白（Aβ，n = 413）整个切片图像和 tau 缠结（n = 639）的数字算法数据与手动病理数据高度相关（r = 0.83 至 0.94）。在不同的放大倍率和重复扫描下，测量结果是稳健且可重复的。即使通过临床诊断对样本进行分层，对多个大脑区域的 Aβ 和 tau 蛋白缠结的数字测量也再现了既定的相关模式。最后，我们将新生成的数字测量值与多项大型尸检研究中的历史测量值进行协调。我们描述了一种多学科方法来开发数字病理学管道，该管道可重复地识别 AD 神经病理学、Aβ 负载和 tau 缠结。数字病理学是一种强大的工具，可以克服与传统显微镜方法相关的关键挑战。