Federated digital pathology platform for AD/ADRD research and diagnostics

University Of Kentucky, Lexington KY

Investigators

Linked publications, trials & patents

Abstract

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.