Abstract
Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
Highlights
The commonest form of dementia is Alzheimer’s disease (AD), with no authenticated disease-modifying treatment [1]
The main objective of this study is to explore the neuroimaging biomarkers and their features, available databases, and pre-processing and input management techniques, Sensors 2021, 21, 7259 the DL and transfer learning (TL) methods that are adept for apprehending the disease-related patterns and perform efficiently even in the case of insufficient neuroimaging data, the accuracy achieved to date, the limitations, and gaps
We started this review with the concept of AD, Mild cognitive impairment (MCI), tailed by the discussion of existing standards for diagnosis of AD by using magnetic resonance imaging (MRI), Functional MRI (fMRI) and positron emission tomography (PET) neuroimaging biomarkers
Summary
The commonest form of dementia is Alzheimer’s disease (AD), with no authenticated disease-modifying treatment [1]. AD progresses gradually for several years before clinical symptoms [1]. It is estimated that approximately 5.8 million individuals in the USA were living with AD in 2020. This number is anticipated to be 14 million by 2050 [2]. It is critical to identify individuals at an initial stage in AD, even before meeting the clinical criteria. MCI is a stage between healthy and AD. A subject with MCI will have changes in their cognitive ability, but still will be able to perform their daily activities. 20% of individuals aged 65 or above have MCI. A confirmed diagnosis of Alzheimer’s disease (AD) can only be identified through autopsy [4]
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