Overview on Incorporating Computer-Aided Diagnosis Systems for Dementia: Early Detection of Alzheimer's Disease

Overview on Incorporating Computer-Aided Diagnosis Systems for Dementia: Early Detection of Alzheimer's Disease

DOI: 10.4018/978-1-6684-6980-4.ch005
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Dementia is one of the major issues in public health all over the world. Alzheimer's disease (AD) is its most common and famous form. Late detection of AD has irreparable effects for the people suffering from it. Cognitive assessment tests are the conventional approach to detect AD. They are quick to do, and not costly. However, they have low predictive values. Therefore, other ways such as magnetic resonance imaging (MRI) are used. Recently, advances in computer-aided diagnosis system (CADS) using MRI have provided useful information in the quantitative evaluation of AD at an early stage. Although it cannot be substituted with the doctors, but it helps. Many algorithms for CADS were presented, which means CADS is one of the growing techniques in this field. Because there is no standardized approach to determine the best one, it is essential to be familiar with general approaches to design a CADS. This chapter deals with a general approach for design and develop a reliable CADS using biomarkers extracted from MRI. The advancement of using CAS and MRI for AD are discussed.
Chapter Preview
Top

Introduction

Alzheimer’s Disease (AD) and Its Diagnostic Approaches

One of the greatest challenges that neuropsychologists have encountered in the last 50 years has been determining the cognitive and behavioral aspects of dementia and their relationship to underlying brain dysfunction (Bondi et al., 2017). With the ageing of the population and the age-related nature of many dementias and neurodegenerative disorders, this problem has become increasingly difficult.

Even though the concept of dementia has existed for many years, the main important clinical syndromes and related changes were first discovered not long ago (Mahandra, 1984).

Dementia is related to more than 70 diverse causes of brain dysfunction, but Alzheimer's disease (AD) is the most common cause for half of all cases (Cumming & Benson, 1992). AD is the foremost common shape of dementia, caused by build-up of beta amyloid plaques within the brain (Alzheimer's Disease Facts and Figures, 2010).

AD is regularly confused with normal aging and dementia. Serious memory loss, characteristic of AD, is not an indication of typical aging (Toepper, 2017). Healthy aging may include the gradual hair loss, weight, height, and muscle mass. It is common to have a slight decrease in memory, such as slower review of information, but cognitive decline that affects standard of daily living is not an ordinary portion of the aging process; it is characterized as the noteworthy loss of cognitive abilities efficiently serious to interfere with social functioning (Pini et al., 2016).

Because AD advances slowly, there are three main stages of the disease, each with its own set of problems and symptoms. Identification of these stages in a patient can help for a better decision; Early-stage AD: this stage lasts 2 to 4 years, it is often when the disease is first diagnosed. In this step, family and friends may start to understand the patient’s cognitive ability decline (Choo et al., 2019).

Moderate AD: this is the longest stage and usually lasts 2 to 10 years. Patients often faced difficult problems regarding memory and daily living activities (Deardorff, Grossberg, 2019).

Severe AD: in this final level, decrement of cognitive capacity continues, and physical ability is severely affected. This stage can last 1 or 3 years and due to the difficulty for families to care for the patient, this step results in nursing home or long-term care in other related places (Block et al., 2016).

Top

Literature Review

There is no standardized approach to determine the best computer-aided diagnosis system (CADS), therefore, it is essential to be familiar with general approaches to design a CADS. Study of different methods for specific application can help to design and develop in an appropriate way. Lately, deep learning techniques for segmenting the structure of the brain and categorizing AD have drawn attention. As a result, deep techniques are now favored over cutting-edge deep learning techniques because they can produce useful results over a vast quantity of data (Khojaste-Sarakhsi et al., 2022). The retrieved features from MRI had a big effect on how well-known earlier methods were (A° et al., 2022). A recent related survey provided the current state of using classification methods on MRI for early detection of AD (Fathi et al., 2022). The review of deep models, modalities, feature extraction strategies showed that most studies have reported the normal control and AD classification with desirable results (Fathi et al., 2022).

Complete Chapter List

Search this Book:
Reset