Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".