Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain’s neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease.

Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging

“…More in-depth discussions surrounding artificial intelligence (AI) and machine learning (ML) were present across all article types within this review [ 78 , 79 , 80 ]. Zhou et al and Minoshima et al summarized the common ML techniques used in radiology such as feed-forward neural networks (FFNN) and convolutional neural networks (CNN) [ 78 , 79 ]. This work showed that most AI studies in hybrid imaging employ a CNN or generative adversarial network (GAN) as a part of the classification model.…”

“…More in-depth discussions surrounding artificial intelligence (AI) and machine learning (ML) were present across all article types within this review [78][79][80]. Zhou et al and Minoshima et al summarized the common ML techniques used in radiology such as feed-forward neural networks (FFNN) and convolutional neural networks (CNN) [78,79].…”

Severity Grading and Early Detection of Alzheimer’s Disease through Transfer Learning

“…It could be used for all phases of Alzheimer's disease as well as to differentiate between healthy people and the ill. Additionally, MRI could be utilized for the detection of mild cognitive impairment (MCI) and Alzheimer's disease (AD), as well as the conversion from MCI to AD. It can also be used to distinguish between distinct forms of dementia, such as frontotemporal dementia and AD, which may exhibit similar clinical symptoms [6,7].…”