ArticleJournal of neuropathology and experimental neurology2021
Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques.
Article in Journal of neuropathology and experimental neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 54 citations in OpenAlex.
- Incident versus prevalent progressive supranuclear palsy: analyses of U.S. Alzheimer's Disease Research Centers data indicate clinical and neuropathological features of a relatively common and underrecognized tauopathy in the aging brain.Acta neuropathologica · 2026Article
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
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- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
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- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Reliability and modeling of digital histological measurements in Alzheimer's disease neuropathologic change and Lewy body disease.Journal of neuropathology and experimental neurology · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 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
- Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation.Free neuropathology · 2025Article
- 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
- Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.Scientific data · 2024Article
- Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.Acta neuropathologica communications · 2023Article
- Accurate digital quantification of tau pathology in progressive supranuclear palsy.Acta neuropathologica communications · 2023Article
- Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience.Journal of neuropathology and experimental neurology · 2023Review
- Case report of a patient with unclassified tauopathy with molecular and neuropathological features of both progressive supranuclear palsy and corticobasal degeneration.Acta neuropathologica communications · 2023Article
- The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers.Journal of neuropathology and experimental neurology · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.