Rosado Aaron M, Vizcarra Juan C, Sharma Shivam R Rai, Keene Cristopher Dirk, White Charles L, Kim Ain, Forrest Shelley L, Kovacs Gabor G, Chuah Chen-Nee, Flanagan Margaret E, Pearce Thomas M, Dugger Brittany N, Gutman David A
Emory University School of Medicine Department of Laboratory Medicine and Pathology, Emory University Hospital, Atlanta, Georgia, USA.
Davis Department of Computer Science, University of California, Davis, California, USA.
Alzheimers Dement. 2025 Nov;21(11):e70775. doi: 10.1002/alz.70775.
A neuropathology examination after death remains the gold standard for differentiating between Alzheimer disease (AD) and AD and related dementias (ADRD). Increasing interest and familiarity with digital imaging highlights recent shifts to modernize pathology workflows by leveraging technology that automates imaging and analysis. This review provides an overview of digital pathology technologies and their associated infrastructure, available open-source and proprietary digital pathology software, relevant background on neurodegenerative histopathological features, and computational research. It further examines recent developments in digital pathology in neurodegenerative disease research with an emphasis on machine learning. We discuss evidence supporting how recently developed technologies and methodologies can enhance our understanding of histopathologic features of neurodegeneration and correlations of histopathologic features with cognitive performance and age at death. Finally, we review potential directions for neurodegenerative disease digital pathology research given trends in technological infrastructure development and other digital pathology research. HIGHLIGHTS: Provides a historical summary of digital pathology with respect to neuropathology. Examines key digital pathology technologies. Explores digital pathology applications in neurodegenerative disease and their contribution to research. Discusses the future of digital neuropathology.
死后进行神经病理学检查仍然是区分阿尔茨海默病（AD）与AD及相关痴呆症（ADRD）的金标准。对数字成像的兴趣日益浓厚且越来越熟悉，这凸显了近期通过利用自动化成像和分析技术使病理学工作流程现代化的转变。本综述概述了数字病理学技术及其相关基础设施、可用的开源和专有数字病理学软件、神经退行性组织病理学特征的相关背景以及计算研究。它进一步探讨了神经退行性疾病研究中数字病理学的最新进展，重点是机器学习。我们讨论了支持最近开发的技术和方法如何增强我们对神经退行性变组织病理学特征的理解以及组织病理学特征与认知表现和死亡年龄之间相关性的证据。最后，鉴于技术基础设施发展趋势和其他数字病理学研究，我们回顾了神经退行性疾病数字病理学研究的潜在方向。要点：提供了数字病理学在神经病理学方面的历史总结。研究关键数字病理学技术。探索数字病理学在神经退行性疾病中的应用及其对研究的贡献。讨论数字神经病理学的未来。
