Brain Digital Slide Archive: An Open Source Whole Slide Image Sharing Platform for AD/ADRD Research and Diagnostics
Bibliographic record
Abstract
BACKGROUND: Neuropathological evaluation of brain tissue remains the gold standard for diagnosing neurodegenerative diseases. However, limited data sharing and variability in sample preparation pose significant challenges. Advances in digital pathology and machine learning offer opportunities to standardize approaches and improve diagnostic accuracy. METHOD: Building on the Digital Slide Archive (DSA), originally designed for cancer imaging, we are developing the NIH U24 funded Brain Digital Slide Archive (BDSA), a federated, open-source platform tailored to support research and diagnostics in neurodegenerative diseases. The BDSA supports whole slide images (WSIs) for Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRDs), adhering to FAIR (findable, accessible, interoperable, reusable) data principles. Metadata associated with WSIs aligns with the National Alzheimer's Coordinating Center (NACC) Data Front Door for enhanced accessibility. Workflows to enable standardized sharing of WSIs, annotations, and metadata, while supporting the development and validation of machine learning algorithms across diverse datasets. RESULT: Initial development includes integrating WSIs from nine AD/ADRD-focused research centers, harmonizing data across institutions, and implementing universal data-sharing agreements. An anonymization tool has been incorporated to scrub labels from WSIs, ensuring donor confidentiality. Required data use agreements and material transfer agreements have been completed for all contributing U.S.-based sites. Administrative best practices, including regular software testing and standardized protocols, are being applied to create a robust and secure platform. The BDSA will provide a unified resource for research, diagnostics, and training, facilitating scalable and reproducible analyses while preserving institutional data control. CONCLUSION: The BDSA aims to democratize access to neuropathological data through a standardized, user-friendly digital repository. By integrating tools for privacy, data sharing, and machine learning, this platform will enhance understanding of neurodegenerative diseases and foster collaboration across institutions. As a federated open-source resource, the BDSA has the potential to transform digital neuropathology, driving innovation in AD/ADRD research and diagnostics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.048 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".