The Different Strategies used for the Early Diagnosis of Alzheimer’s Disease
DOI:
https://doi.org/10.51983/ajeat-2019.8.1.1064Keywords:
Alzheimer’s Disease, Gerontology, Early Diagnosis, Biomarkers, NeuroimagingAbstract
Gerontology or the scientific study of old age deal with the many clinical problems that are common in the elderly population and many of these follow the orthodox pattern of clinical practice. Patients characteristically have poor insight and often attribute their early symptoms of amnesia to normal aging. Alzheimer’s disease (AD) is a common form of senile dementia that makes disabilities in cognitive behavior and performs routine functions. There are several causes for the disease. Although our understanding of the key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by the patient. The aim of this paper is to give an overall idea of the hallmarks, stages of the disease, signs or symptoms and the different methods used for its diagnosis. Any disease-modifying treatments which are developed are most likely to be successful if initiated early in the process, and this requires that we develop reliable, validated and economical ways to diagnose Alzheimer’s−type pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. There are several clinical tests and neuroimaging techniques used in clinical practice for the diagnosis of Alzheimer’s – type pathology. Prominent of them are biomarkers, Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET) and Single−Proton CT Scanning (SPECT). Using the new advanced Biomedical Engineering Technologies to the clinical practices stated above, we can develop a computer-aided tool for the early diagnosis of AD. The different soft computing tools in Biomedical Engineering for developing a computer-aided tool are Neural Networks, Genetic algorithm, Wavelet Networks, Support Vector Machines, and Fuzzy Logic. In this paper, we have focused on the different causes as well as the different strategies used for the early diagnosis of Alzheimer’s disease (AD).
References
D. S. Knopman et al., "Longitudinal study of death and institutionalization in patients with primary degenerative dementia," J Am GeriatrSoc, vol. 36, pp.108-112, 1988.
J. L. Cummings et al., "Alzheimer’s disease: etiologies, pathophysiology, cognitive reserve and treatment opportunities," Neurology, vol. 51, pp. 2-17, 1998.
C. S. Sandeep and A. Sukesh Kumar, "The Early Confirmation of Alzheimer’s Disease using Internet Sources," Asian Journal of Science and Applied Technology, vol. 6, no. 1, pp. 10-17, 2017.
C. Wolfson et al., "A reevaluation of the duration of survival after the onset of dementia," N Engl J Med, vol. 344, no. 15, pp. 1111-6, 2001.
G. K. Wilcock and M. M. Esiri, "Plaques, tangles, and dementia. A quantitative study," J NeurolSci, vol. 56, pp. 343-56, 1982.
C. S. Sandeep et al., "Analysis of MRI and OCT Images for the Early Diagnosis of Alzheimer’s Disease Using Wavelet Networks," AMSE journal on Lectures on Modelling and Simulation, vol. 1, pp. 31-40, 2018.
P. V. Arriagada et al., "Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease," Neurology, vol. 42, no. pp. 631-9, 1992.
C. S. Sandeep et al., "Analysis of Retinal OCT Images for the Early Diagnosis of Alzheimer’s Disease," Springer-Advances in Intelligent Systems and Computing book series (AISC), vol. 749, pp. 509-520, 2018.
M. A. Westerman et al., "The relationship between Aβ and memory in the Tg2576 mouse model of Alzheimer’s disease," Journal of Neuroscience, vol. 22, no. 5, pp. 1858-67, 2002.
A. Ott et al., "Diabetes mellitus and the risk of dementia: The Rotterdam study," Neurology, vol. 53, no. 9, pp. 1937-42, 1999.
I. Skoog et al., "15-year longitudinal study of blood pressure and dementia," Lancet, vol. 347, pp. 141-5, 1996.
C. S. Sandeep and A. Sukesh Kumar, "A Psychometric Assessment Method for the Early Diagnosis of Alzheimer’s disease," International Journal of Scientific & Engineering Research –IJSER, vol. 8, no. 3, pp. 901-905, 2017.
D. A. Snowdon et al., "Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: Findings from the nun study," J Am Med Assoc., vol. 275, no. 7, pp. 528-32, 1996.
R. Yaari and J. Corey-Bloom, "Alzheimer’s disease: Pathology andpathophysiology," Semin Neurol., vol. 27, pp. 32-41, 2007.
C. S. Sandeep and A. Sukesh Kumar, "A Review on the Early Diagnosis of Alzheimer’s Disease (AD) through Different Tests, Techniques and Databases," AMSE Journals: Modelling C, vol. 76, no. 1, pp. 1-22, 2015.
E. B. Larson et al., "Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older," Ann Intern Med., vol. 144, pp. 73-81, 2006.
R. J. Harvey et al., "The prevalence and causes of dementia in people under the age of 65 years," J NeurolNeurosurg Psychiatry, vol. 74, pp. 1206-9, 2003.
L. A. Farrer et al., "Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease, A meta-analysis," JAMA, vol. 278, pp. 1349-56, 1997.
C. P. Ferri et al., "Global prevalence of dementia: a Delphi consensus study," Lancet, vol. 366, pp. 2112-7, 2005.
C. S. Sandeep et al., "The Online Datasets Used to Classify the Different Stages for the Early Diagnosis of Alzheimer’s Disease (AD)," International Journal of Engineering and Advanced Technology, vol. 6, no. 4, pp. 38-45, 2017.
H. F. Chiu et al., "Prevalence of dementia in Chinese elderly in Hong Kong," Neurology, vol. 50, pp. 1002-9, 1998.
L. X. Hy and D. M. Keller, "Prevalence of AD among whites: a summary by levels of severity," Neurology, vol. 55, pp. 198-204, 2000.
L. W. Chu et al., "Bioavailable testosterone predicts a lower risk of Alzheimer’s disease in older men," J Alzheimers Dis, vol. 21, pp. 1335-45, 2010.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.