scholarly journals An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4182
Author(s):  
Haijing Sun ◽  
Anna Wang ◽  
Wenhui Wang ◽  
Chen Liu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Spatial Information ◽  
Feature Space ◽  
Attention Mechanism ◽  
Residual Network ◽  
Long Distance ◽  
Distance Correlation ◽  
Non Local

The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images

Neuroscience ◽  
2021 ◽  
Author(s):  
Atif Mehmood ◽  
Shuyuan yang ◽  
Zhixi feng ◽  
Min wang ◽  
AL Smadi Ahmad ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Transfer Learning ◽  
Learning Approach

scholarly journals The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer’s Disease and Parkinson’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 371
Author(s):  
Patrycja Pawlik ◽  
Katarzyna Błochowiak
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Diagnostic Tool ◽  
Clinical Symptoms ◽  
Early Screening ◽  
Salivary Biomarkers

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

scholarly journals Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer’s Disease: A Review

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jessica Beltrán ◽  
Mireya S. García-Vázquez ◽  
Jenny Benois-Pineau ◽  
Luis Miguel Gutierrez-Robledo ◽  
Jean-François Dartigues
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Movements ◽  
Early Diagnosis ◽  
Future Research ◽  
Computational Techniques ◽  
Research Directions ◽  
Future Research Directions ◽  
Tools And Techniques

An opportune early diagnosis of Alzheimer’s disease (AD) would help to overcome symptoms and improve the quality of life for AD patients. Research studies have identified early manifestations of AD that occur years before the diagnosis. For instance, eye movements of people with AD in different tasks differ from eye movements of control subjects. In this review, we present a summary and evolution of research approaches that use eye tracking technology and computational analysis to measure and compare eye movements under different tasks and experiments. Furthermore, this review is targeted to the feasibility of pioneer work on developing computational tools and techniques to analyze eye movements under naturalistic scenarios. We describe the progress in technology that can enhance the analysis of eye movements everywhere while subjects perform their daily activities and give future research directions to develop tools to support early AD diagnosis through analysis of eye movements.

Predicting Alzheimer's Disease Progression by Combining Multiple Measures

2021 ◽  
Author(s):  
Nour Zawawi ◽  
Heba Gamal Saber ◽  
Mohamed Hashem ◽  
Tarek F.Gharib
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cell Death ◽  
Early Diagnosis ◽  
Nerve Cell ◽  
Neurological Examination ◽  
Medical Data ◽  
Complex Interactions ◽  
Dementia Patients ◽  
Nerve Cell Death

Alzheimer's disease (AD) is a degenerative brain ailment that affects millions worldwide. It is the most common form of dementia. Patients with an early diagnosis of Alzheimer's disease have a strong chance of preventing additional brain damage by halting nerve cell death. At the same time, it begins to progress several years before any symptoms appear. The variety of data is the biggest problem encountered during diagnosis. Neurological examination, brain imaging, and often asked questions from his connected closed relatives are the three forms of data that a neurologist or geriatrics employs to diagnose patients. One of the biggest questions which need answering is the choice of a convenient feature. The main objective of this paper is to help neurologists or geriatricians diagnose patient conditions. It proposes a new hybrid model for features extracted from medical data. It discusses AD's early diagnosis and progression for all features considered in the diagnosis and their complex interactions. It proves to have the best accuracy when compared with the state-ofthe-art algorithm. Also, it proves to be more accurate against some recent research ideas. It got 95% in all cases, considering this work focused more on increasing the number of instances in comparison.

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