ArticleJournal of Alzheimer's disease : JAD2021
Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
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- The ABC transporter A7 modulates neuroinflammation via NLRP3 inflammasome in Alzheimer's disease mice.Alzheimer's research & therapy · 2025Article
- Physiological expression of mutated TAU impaired astrocyte activity and exacerbates β-amyloid pathology in 5xFAD mice.Journal of neuroinflammation · 2023Article
- Time- and Sex-Dependent Effects of Fingolimod Treatment in a Mouse Model of Alzheimer's Disease.Biomolecules · 2023Article
- A Novel Huntington's Disease Assessment Platform to Support Future Drug Discovery and Development.International journal of molecular sciences · 2022Article
- Isotope-labeled amyloid-β does not transmit to the brain in a prion-like manner after peripheral administration.EMBO reports · 2022Article
- Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer's disease.Journal of neuroinflammation · 2022Article
- A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model.Frontiers in cellular neuroscience · 2022Article
- A New Tool for the Analysis of the Effect of Intracerebrally Injected Anti-Amyloid-β Compounds.Journal of Alzheimer's disease : JAD · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIO™ for automatic analysis of histological whole-slide images using machine learning/artificial intelligence.
objectiveTo evaluate and validate the use of DeePathology STUDIO for the analysis of histological slides at high resolution.
methodsWe compared the DeePathology STUDIO and our current standard method using macros in AxioVision for the analysis of amyloid-β (Aβ) plaques and microglia in APP-transgenic mice at different ages. We analyzed density variables and total time invested with each approach. In addition, we correlated Aβ concentration in brain tissue measured by ELISA with the results of Aβ staining analysis.
resultsDeePathology STUDIO showed a significant decrease of the time for establishing new analyses and the total analysis time by up to 90%. On the other hand, both approaches showed similar quantitative results in plaque and activated microglia density in the different experimental groups. DeePathology STUDIO showed higher sensitivity and accuracy for small-sized plaques. In addition, DeePathology STUDIO allowed the classification of plaques in diffuse- and dense-packed, which was not possible with our traditional analysis.
conclusionDeePathology STUDIO substantially reduced the effort needed for a new analysis showing comparable quantitative results to the traditional approach. In addition, it allowed including different objects (categories) or cell types in a single analysis, which is not possible with conventional methods.
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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.