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Alzheimer's Disease: Treatment

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...

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Slice-selective learning for Alzheimer's disease classification using a generative adversarial network: a feasibility

Han Woong Kim¹, Ha Eun Lee¹, Sangwon Lee²

¹Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.

European Journal of Nuclear Medicine and Molecular Imaging

|January 26, 2020

Summary

This study developed a Generative Adversarial Network model for Alzheimer's disease (AD) detection, achieving high accuracy by selecting specific PET imaging slices. The model demonstrates consistent performance across different acquisition environments, validating its feasibility for external use.

Keywords:

Alzheimer’s disease External validation Feasibility study Generative Adversarial Network [18F] FDG PET/CT

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Positron Emission Tomography (PET) imaging is crucial for diagnosing Alzheimer's disease (AD).
Variations in PET imaging acquisition environments can negatively impact the performance of machine learning models.
Developing robust models less sensitive to environmental differences is essential for reliable external validation.

Purpose of the Study:

To assess the feasibility of using slice selective learning with a Generative Adversarial Network (GAN) for external validation of Alzheimer's disease (AD) detection.
To develop a model that is less sensitive to variations in PET imaging acquisition environments.
To evaluate the performance of individual slice selection in improving model accuracy.

Main Methods:

Trained a Boundary Equilibrium Generative Adversarial Network (BEGAN) on [18F]FDG PET/CT data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Severance Hospital datasets.
Applied slice selective learning to extract unbiased features, reducing computational cost.
Utilized extracted features to train a support vector machine (SVM) classifier for distinguishing AD from normal cognition (NC).

Main Results:

The optimal performance was achieved using double slices covering the posterior cingulate cortex (PCC).
The proposed network demonstrated high accuracy (94.33-94.82%), sensitivity (91.78-92.11%), and specificity (97.06-97.45%) on both independent datasets.
A statistically significant difference in performance was observed between using two slices versus one slice (p < 0.05).

Conclusions:

The developed model successfully learned generalized features of AD and NC, enabling reliable external validation.
The study confirms the feasibility of the slice selective learning approach, showing consistent performance across diverse imaging environments.
This method offers a promising strategy for robust AD detection using PET imaging.