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Positron Emission Tomography01:29

Positron Emission Tomography

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Related Experiment Video

Updated: Jun 11, 2025

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

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

Eui Jung An1, Jin Beom Kim1, Junik Son1

  • 1Department of Nuclear Medicine, Kyungpook National University Hospital.

Nuclear Medicine Communications
|October 1, 2024
PubMed
Summary
This summary is machine-generated.

A deep learning model accurately classifies amyloid plaque deposition in brain PET images for Alzheimer's disease diagnosis. This convolutional neural network (CNN) shows high reliability, potentially aiding clinical decisions.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Alzheimer's Disease Diagnostics

Background:

  • Alzheimer's disease diagnosis relies on identifying amyloid plaque deposition.
  • Positron Emission Tomography (PET) imaging is crucial for visualizing amyloid plaques.
  • Accurate classification of PET images is essential for timely diagnosis and treatment.

Purpose of the Study:

  • To investigate the efficacy of a deep learning model, specifically a convolutional neural network (CNN), for classifying amyloid plaque deposition in brain PET images.
  • To assess the CNN model's ability to differentiate between amyloid-positive and amyloid-negative cases in patients suspected of Alzheimer's disease.

Main Methods:

  • Retrospective analysis of brain amyloid 18F-florapronol PET/CT images from 175 patients (2019-2022).
  • Visual assessment by nuclear medicine specialists classified images as positive or negative.
  • A CNN model was trained and evaluated using stratified 5-fold cross-validation and a held-out test set, with data augmentation via image rotation.

Main Results:

  • The CNN model achieved an average accuracy of 0.917 ± 0.027 across cross-validation folds.
  • In the testing set, the model demonstrated an accuracy of 0.914 and an Area Under the Curve (AUC) of 0.958.
  • These results indicate the model's high reliability in distinguishing between amyloid-positive and negative cases.

Conclusions:

  • The developed CNN model shows significant potential for accurately classifying amyloid positivity in brain PET images.
  • This deep learning approach can serve as a valuable supplementary tool to improve the accuracy of clinical diagnoses for Alzheimer's disease.
  • Further validation could integrate this AI tool into routine diagnostic workflows.