Federated digital pathology platform for AD/ADRD research and diagnostics

项目摘要/摘要 我们将连接多个阿尔茨海默病和相关痴呆症(AD/ADRD)研究中心，以实现优化和标准化的整体幻灯片图像(WSI)高级分析。我们提出了这些具体目标：具体目标1：建立一个用于人类数字神经病理(DNP)切片数据共享和分析的联合平台。具体目标1a：开发一个开放源码平台，将分布在多个储存库的数据汇聚到一个中央登记处门户。这个子目标将遵循联邦数据管理、协调、注释和标准化，采用公平(可查找、可访问、可互操作和可重用)原则。具体目标1b：开发联邦数据管理和管理系统。此子目标提供了跨联合站点管理物理数据(如数字图像)的方法。WSI数据和元数据将与专用存储库和高性能集群共享。具体目标2：开发和演示联合机器学习/人工智能(ML/AI)、结果评估和中央项目信息平台，构成AD/ADRD研究的管理中心。具体目标2a：开发联合数据标注和自动化AI/ML处理系统。这个子目标为用户提供了准备人工智能就绪多模式(信息社会信息系统、元数据、人口统计数据等)的方法。来自分布式来源的数据集，并进行多站点数据传输和联合培训。在AIM 1中开发的服务和工具将用于以编程方式生成和填充用户定义的AI/ML管道。具体目标2b：开发一个用于生成和审查数据集及相关模型的开放源码平台。这一分目标将提供一个项目评估门户，将队列、数据集和模型培训结果整合到一个统一的界面中。一个面向公众的模型中心将提供项目、数据、规范和模型数据，以支持数据共享和分析需求。为了优化和展示新型联邦网络的优势，提出了五个集成方案。这些将跨越人类人口和疾病的不同样本，以利用DNP的独特优势。以下网站将提供资源、专业知识、实验研究设计、数据和分析：肯塔基大学(Pis Nelson和Bumgardner，Co-is Cheung和Fardo)。生成用于DNP诊断和研究的标准化SOP以及联合网络的ML/AI专业知识。西北大学/修女研究和UTSA大学(Pi Flanagan)。关注以社区为基础的队列和不同种族人群中的TDP-43蛋白病。圣保罗大学S分校和加州大学旧金山分校(苏门托分校和格林伯格分校)。从具有非洲血统和非非洲血统的个体的大脑中评估ADNC。多伦多大学(Co-I Kovacs)：使用ML研究白质tau病理变化，以获得对多种tauopathy的新见解。华盛顿大学(Co-I Keene)：研究不同年龄和环境暴露的tau病理，重点是创伤性脑损伤。

Project Summary/Abstract We will connect multiple Alzheimer’s disease and related dementias (AD/ADRD) research centers for optimized and standardized whole slide image (WSI) advanced analytics. We propose these Specific Aims: Specific Aim 1: Generate a federated platform for data sharing and analysis of human digital neuropathological (DNP) slides. Specific Aim 1a: Develop an open-source platform to aggregate data distributed across multiple repositories into a central registry portal. This subaim will follow federated data curation, harmonization, annotation, and standardization employing FAIR (findable, accessible, interoperable, and reusable) principles. Specific Aim 1b: Develop a federated data curation and management system. This subaim provides methods to curate physical data, such as digital images, across federated sites. WSI data and metadata will be shared with private repositories and high-performance clusters. Specific Aim 2: Develop and demonstrate a platform for federated machine learning/artificial intelligence (ML/AI), result evaluation, and central project information, constituting a management hub for AD/ADRD studies. Specific Aim 2a: Develop a federated data annotation and automated AI/ML processing system. This subaim provides methods for users to prepare AI-ready multimodal (WSIs, metadata, demographics, etc.) datasets from distributed sources and conduct multi-site data transfer and federated training. Services and tools developed in Aim 1 will be used to programmatically generate and populate user-defined AI/ML pipelines. Specific Aim 2b: Develop an open-source platform for the generation and review of datasets and associated models. This subaim will provide a project evaluation portal integrating cohort, dataset, and model training results in a unified interface. A public-facing model hub will provide project, data, specifications, and model data to support data sharing and analysis requirements. To optimize and demonstrate the strengths of the novel federated network, five integrated projects are proposed. These will span a diverse sampling of human populations and diseases to leverage the unique strengths of DNP. The following sites will contribute resources, expertise, experimental study design, data, and analyses: University of Kentucky (PIs Nelson and Bumgardner, Co-Is Cheung and Fardo). Generate standardized SOPs useful for DNP diagnoses and research along with ML/AI expertise for a federated network. Northwestern/Nun Study and UTSA Universities (PI Flanagan). A focus on TDP-43 proteinopathy in community-based cohorts and ethno-racially diverse populations. Universityof São Paulo and UCSF (Co-I Suemoto and OSC Grinberg). Evaluate ADNC from the brains of individuals with African and non-African ancestries. University of Toronto (Co-I Kovacs): Use ML to study white matter tau pathologic changes for novel insights into multiple tauopathies. University of Washington (Co-I Keene): Study tau pathologies across ages and environmental exposures with a focus on traumatic brain injuries.