AIMC Topic: Stilbenes
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Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.
Scientific reports
Jul 1, 2025
This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical data to improve diagnostic accuracy. Furthermore, the study explored machine learning models to pre...
Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.
Journal of imaging informatics in medicine
Oct 2, 2024
Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model definition. Neural architecture search (NAS) automates this process, reducing human intervention signifi...
Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.
Analytical chemistry
May 3, 2024
Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene der...
Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.
PloS one
Oct 20, 2021
High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. The...
Increasing the confidence of F-Florbetaben PET interpretations: Machine learning quantitative approximation.
Revista espanola de medicina nuclear e imagen molecular
Apr 21, 2021
AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of F-Florbetaben brain deposition.
Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.
NeuroImage
Feb 19, 2021
It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amyloid PET acquired with different radiotracers through image-level translation. Deep genera...
Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.
European journal of nuclear medicine and molecular imaging
Sep 29, 2020
PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learnin...
Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology
Dec 11, 2018
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...
Aggregation-Induced Emission Probe for Specific Turn-On Quantification of Soluble Transferrin Receptor: An Important Disease Marker for Iron Deficiency Anemia and Kidney Diseases.
Analytical chemistry
Dec 22, 2017
Transferrin receptor (TfR) is overexpressed on the surface of many cancer cells due to its vital roles in iron circulation and cellular respiration. Soluble transferrin receptor (sTfR), a truncated extracellular form of TfR in serum, is an important ...
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