AIMC Topic: Ethylene Glycols
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A deep learning model for generating [F]FDG PET Images from early-phase [F]Florbetapir and [F]Flutemetamol PET images.
European journal of nuclear medicine and molecular imaging
Jun 11, 2024
INTRODUCTION: Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunct...
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...
Classification of amyloid status using machine learning with histograms of oriented 3D gradients.
NeuroImage. Clinical
May 10, 2016
Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, howe...
Ethylene Glycols
Male
Amyloid
Reproducibility of Results
Support Vector Machine
Alzheimer Disease
Image Interpretation, Computer-Assisted
Humans
Radiopharmaceuticals
Aniline Compounds
Carbon Radioisotopes
Aged
Brain
Positron-Emission Tomography
Aged, 80 and over
Female
Stilbenes
Sensitivity and Specificity
Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.
Human brain mapping
Jun 1, 2025
There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four indiv...
The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score.
Clinical nuclear medicine
Jun 1, 2020
PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapir and F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score.
The Use of Random Forests to Classify Amyloid Brain PET.
Clinical nuclear medicine
Oct 1, 2019
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
Positive reactions to pairs of allergens associated with polysensitization: analysis of IVDK data with machine-learning techniques.
Contact dermatitis
Apr 1, 2017
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