scholarly journals Towards the Early Diagnosis of Alzheimer's Disease through the Application of a Multicriteria Classification Model

10.5772/38425 ◽  
2012 ◽  
Author(s):  
Amaury Brasil ◽  
Plcido Rogrio ◽  
Andr Lus Vasconcelos Coelho
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Classification Model

Using GANs and MLP Artificial Neural Networks to support early diagnosis of Alzheimer’s disease: a study on the potential of artificial data expansion

2021 ◽  
Author(s):  
Jonathan Bandeira ◽  
Mêuser Valença ◽  
Renan Alencar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Computational Intelligence ◽  
Traditional Approach ◽  
Developed Countries ◽  
Classification Model ◽  
World Health ◽  
Age Related ◽  
Artificial Neural

The life expectancy of the population in the most developed countries is growing every day and, consequently, there is an increase in various age-related diseases. In Brazil, just over 1.1 million people have Alzheimer’s disease (AD). In 2019, according to the World Health Organization, Alzheimer’s disease and other dementias were the third leading cause of mortality in the Americas and Europe. Despite being a degenerative and irreversible disease, if diagnosed early, treatments can be performed to slow the progression of symptoms and ensure a better quality of life for the patient. Most papers that study Computational Intelligence solutions to support diagnosis follow an approach based on neuroimaging evidence. In addition to this, another approach that has been gaining prominence is biochemical and molecular analysis. Following this approach, Ray et al., Ravetti & Moscato and Dantas & Valença carried out studies with classifiers from statistics or Computational Intelligence to support the early diagnosis of the disease. The work was carried out from a dataset with values of 120 blood proteins. Through this, they were able to classify whether or not the patient could be diagnosed with AD. As a result, Ray et al., Ravetti & Moscato and Dantas & Valença obtained an average accuracy rate of 89%, 93% and 94.34%, respectively. Thus, this work aims to use a traditional approach with a proposed Multilayer Perceptron Artificial Neural Network model to perform the early diagnosis of a patient with or without AD and compare the results obtained with the results of the related works cited. In addition, this work has as main objective to evaluate the potential of using synthetic data generated using a Generative Adversarial Network in the training and tests of the proposed classification model.

Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve

2010 ◽  
Vol 182 (12) ◽  
Author(s):  
O Querbes ◽  
F Aubry ◽  
J Pariente ◽  
JA Lotterie ◽  
JF Demonet ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Cortical Thickness ◽  
Cognitive Reserve

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 Editorial: Designing a Protocol Adopting an Artificial Intelligence (AI)–Driven Approach for Early Diagnosis of Late-Onset Alzheimer’s Disease

2021 ◽  
Author(s):  
Victor O. K. Li ◽  
Jacqueline C. K. Lam ◽  
Yang Han ◽  
Lawrence Y. L. Cheung ◽  
Jocelyn Downey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Late Onset

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.

Early diagnosis model of Alzheimer’s disease based on sparse logistic regression with the generalized elastic net

2021 ◽  
Vol 66 ◽  
pp. 102362
Author(s):  
Ruyi Xiao ◽  
Xinchun Cui ◽  
Hong Qiao ◽  
Xiangwei Zheng ◽  
Yiquan Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Early Diagnosis ◽  
Elastic Net ◽  
Sparse Logistic Regression ◽  
Model Of Alzheimer’S Disease ◽  
Diagnosis Model

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.

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