Image processing: an early detection tool for Alzheimer’s disease

Ballav, Sangeeta; Biswas, Bini; Dey, Saurabh; Sahu, Vishal Kumar; Basu, Soumya

doi:10.1016/b978-0-323-95539-3.00001-6

Cited by 7 publications

(2 citation statements)

References 130 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…27 In response to this challenge, researchers have sought to develop alternative, cost-effective methods for estimating DTI measurements, thereby facilitating early dementia diagnosis without the financial burden associated with conventional neuroimaging techniques. 28 Researchers have explored alternative approaches that use affordable and readily available factors as potential dementia risk indicators. Factors such as lifestyle for brain health (LIBRA) or brain atrophy and lesion indices (BALI) have been shown to be associated with brain health and cognitive function.…”

Section: Introductionmentioning

confidence: 99%

“…Factors such as lifestyle for brain health (LIBRA) or brain atrophy and lesion indices (BALI) have been shown to be associated with brain health and cognitive function. [28][29][30][31] The aim of this study was to develop an innovative system identification network using a multilayer perceptron (MLP) method to estimate DTI metrics based on a subset of the most influential factors of BALI and LIBRA. Using a data-driven approach, this study sought to identify effective variables from a combined set of BALI and LIBRA factors that can serve as input to the MLP network to predict DTI measures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

View full text Add to dashboard Cite

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

View full text Add to dashboard Cite

show abstract

Deep neural networks for the early diagnosis of dementia and Alzheimer’s disease from MRI images

Wang

2024

Evolving Systems

View full text Add to dashboard Cite

Introduction to Alzheimer's Disease, Biomarkers, and the AI Revolution

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in a steady decline in cognitive ability and memory function. As society ages, the need for an optimum AD management strategy becomes more important. This chapter analyzes the stage-construction etiology and escalating symptoms of AD discovered throughout this review, as well as the identification of the barrier to precise diagnosis. The ability of artificial intelligence to achieve more precise and quicker detection through machine learning, data analytics, and predictive modeling is also being considered. Therefore, employing AI in AD-related studies is a novel approach to enhancing patient outcomes. Proper diagnosis of the parallel increased probability of many parameters is one of the most difficult AD moments to identify. However, the use of artificial intelligence evaluation sensor network technologies and big data analysis has advanced, and preventive instruments can be used. Thus, technology gives humanity hope to stop or, at the very least, slow down this tragedy.

show abstract

Image processing: an early detection tool for Alzheimer’s disease

Cited by 7 publications

References 130 publications

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Deep neural networks for the early diagnosis of dementia and Alzheimer’s disease from MRI images

Introduction to Alzheimer's Disease, Biomarkers, and the AI Revolution

Contact Info

Product

Resources

About