ArticleActa neuropathologica communications2023
Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.
Article in Acta neuropathologica communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evaluating the Transferability of Pathology Foundation Models Across Cancer-related H&E Neurodegeneration-related Immunohistochemical Classification Tasks.bioRxiv : the preprint server for biology · 2026Article
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
- Visual versus quantitative tau-PET Braak staging in Alzheimer's disease using [European journal of nuclear medicine and molecular imaging · 2026Article
- Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.Journal of neuropathology and experimental neurology · 2026Article
- Convolutional neural network-derived neurofibrillary tangle classifiers: Investigative tools to identify maturation levels and explore post-translational modifications using laser capture microdissection coupled mass spectrometry.Journal of neuropathology and experimental neurology · 2026Article
- Review
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Celebrating neuropathology's contributions to Alzheimer's Disease Research Centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Digital pathology in tau research: A comparison of QuPath and HALO.Journal of neuropathology and experimental neurology · 2025Article
- The Neurofibrillary Tangle Maturity Scale: A Novel Framework for Tangle Pathology Evaluation in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.Journal of neuropathology and experimental neurology · 2025Article
- Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer's disease continuum.Acta neuropathologica · 2024Article
- Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software.Journal of neuropathology and experimental neurology · 2024Article
- Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.Acta neuropathologica communications · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.
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