ArticleNeuropathology and applied neurobiology2022
Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.
Article in Neuropathology and applied neurobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- 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
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.Scientific reports · 2026Article
- Article
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- 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
- Early diagnosis of alzheimer's disease using PET imaging and deep learning with comparative data augmentation techniques.Scientific reports · 2025Article
- Age-informed, attention-based weakly supervised learning for neuropathological image assessment.Brain informatics · 2025Article
- 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
- Modeling Alzheimer's Disease: A Review of Gene-Modified and Induced Animal Models, Complex Cell Culture Models, and Computational Modeling.Brain sciences · 2025Review
- Parkinsonism in Alzheimer's disease without Lewy bodies in association with nigral neuron loss: A data-driven clinicopathologic study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The Nun Study: Insights from 30 years of aging and dementia research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.Current topics in medicinal chemistry · 2025Review
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- Novel deep learning for multi-class classification of Alzheimer's in disability using MRI datasets.Frontiers in bioinformatics · 2025Article
- Insights into AlphaFold's breakthrough in neurodegenerative diseases.Irish journal of medical science · 2024Review
- 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
Corrections and comments
- Commented on by
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
aimsThis study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostained digital slide images.
methodsWe trained the YOLOv3 object detection algorithm to detect five tau lesion types: neuronal inclusions, neuritic plaques, tufted astrocytes, astrocytic plaques and coiled bodies. We used 2522 digital slide images of CP13-immunostained slides of the motor cortex from 10 cases each of AD, PSP and CBD for training. Data augmentation was performed to increase the size of the training dataset. We next constructed random forest classifiers using the quantitative burdens of each tau lesion from motor cortex, caudate nucleus and superior frontal gyrus, ascertained from the object detection model. We split 120 cases (32 AD, 36 PSP, 31 CBD and 21 PiD) into training (90 cases) and test (30 cases) sets to train random forest classifiers.
resultsThe resultant random forest classifier achieved an average test score of 0.97, indicating that 29 out of 30 cases were correctly diagnosed. A validation study using hold-out datasets of CP13- and AT8-stained slides from 50 cases (10 AD, 17 PSP, 13 CBD and 10 PiD) showed >92% (without data augmentation) and >95% (with data augmentation) diagnostic accuracy in both CP13- and AT8-stained slides.
conclusionOur diagnostic model trained with CP13 also works for AT8; therefore, our diagnostic tool can be potentially used by other investigators and may assist medical decision-making in neuropathological diagnoses of tauopathies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.