AIMC Topic: Plaque, Amyloid

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Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.

Nuclear medicine communications Sep 27, 2024
PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease.
Deep Learning Image Processing, Computer-Assisted Positron Emission Tomography Computed Tomography Alzheimer Disease Aged Aged, 80 and over Male Middle Aged Positron-Emission Tomography Plaque, Amyloid Humans Retrospective Studies Female
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Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning.

Nature communications Sep 12, 2024
Systemic amyloidosis involves the deposition of misfolded proteins in organs/tissues, leading to progressive organ dysfunction and failure. Congo red is the gold-standard chemical stain for visualizing amyloid deposits in tissue, showing birefringenc...
Staining and Labeling Myocardium Microscopy, Fluorescence Amyloid Humans Deep Learning Plaque, Amyloid Amyloidosis Birefringence Optical Imaging Microscopy, Polarization Congo Red
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Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Acta neuropathologica communications Aug 17, 2024
Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveragi...
Gray Matter White Matter Plaque, Amyloid Temporal Lobe Tissue Banks Humans Machine Learning Male Middle Aged Amyloid beta-Peptides Female Alzheimer Disease Aged Aged, 80 and over
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Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on F-Florbetapir PET Using ADNI Data.

AJNR. American journal of neuroradiology Jun 4, 2020
BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imagi...
Fluorine Radioisotopes Plaque, Amyloid Ethylene Glycols Aniline Compounds Positron-Emission Tomography Humans Neuroimaging Deep Learning Brain Image Processing, Computer-Assisted Female Radiopharmaceuticals Aged Alzheimer Disease Male
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Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

Magnetic resonance imaging Apr 5, 2019
PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque c...
Necrosis Carotid Stenosis Plaque, Amyloid Carotid Artery, Common Plaque, Atherosclerotic Angiography Male Lipids Middle Aged Humans Cerebral Hemorrhage Female Contrast Media Machine Learning Reproducibility of Results Bayes Theorem Calcinosis Algorithms Aged Carotid Arteries Sensitivity and Specificity
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction.

Alzheimer's research & therapy May 31, 2025
BACKGROUND: Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's diseas...
Brain Female Aged Aged, 80 and over Humans Neural Networks, Computer Cognition Magnetic Resonance Imaging Alzheimer Disease Male Plaque, Amyloid Aging Middle Aged Deep Learning
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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 Sep 1, 2024
Neuropathological diagnosis of Alzheimer disease (AD) relies on semiquantitative analysis of phosphorylated tau-positive neurofibrillary tangles (NFTs) and neuritic plaques (NPs), without consideration of lesion heterogeneity in individual cases. We ...
Neurofibrillary Tangles Plaque, Amyloid Software Brain Male Humans Female Deep Learning Aged Image Processing, Computer-Assisted Alzheimer Disease
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Combination of deep learning and 2D CARS figures for identification of amyloid-β plaques.

Optics express Oct 9, 2023
In vivo imaging and accurate identification of amyloid-β (Aβ) plaque are crucial in Alzheimer's disease (AD) research. In this work, we propose to combine the coherent anti-Stokes Raman scattering (CARS) microscopy, a powerful detection technology fo...
Mice Brain Spectrum Analysis, Raman Plaque, Amyloid Animals Amyloid beta-Peptides Deep Learning Nonlinear Optical Microscopy Alzheimer Disease
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Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques.

Journal of neuropathology and experimental neurology Mar 22, 2021
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 immunohistochem...
Plaque, Amyloid Humans Deep Learning Astrocytes Proof of Concept Study
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Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia.

Journal of Alzheimer's disease : JAD Jan 1, 2021
BACKGROUND: Detailed 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 automa...
Animals Image Processing, Computer-Assisted Mice Disease Models, Animal Microglia Alzheimer Disease Mice, Transgenic Plaque, Amyloid Machine Learning
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