Detection of Alzheimer’s disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

Kulasiri, Don; Aberathne, Iroshan; Samarasinghe, Sandhya

doi:10.4103/1673-5374.367840

Cited by 15 publications

(4 citation statements)

References 84 publications

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“…Calcium dysregulation seems to generate large amounts of tau 27 . The inflammatory hypothesis is based on the observation that the increased inflammatory markers in AD patients, such as pro-inflammatory cytokines interleukin-1β, (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor α (TNF α) 28 . Increased oxidative stress in the AD brain is characterized by increased lipid peroxidation, increased protein, and DNA oxidation, these data support the oxidative stress hypothesis 29,30 .…”

Section: The Pathogenesis Of Admentioning

confidence: 99%

“…Increased oxidative stress in the AD brain is characterized by increased lipid peroxidation, increased protein, and DNA oxidation, these data support the oxidative stress hypothesis 29,30 . Mitochondrial disturbances are also associated with both oxidative stress and AD plaque formation 28 .…”

Section: The Pathogenesis Of Admentioning

confidence: 99%

“…AI aids early detection and diagnosis based on a variety of machine learning-trained models. Based on the cognitive tests, imaging techniques, and CSF test, AI can improve the accuracy of traditional diagnostic methods, such as analyzing PET and MRI scan images 28 , and new biomarkers analysis beside Aβ42 and P-tau using the CSF database 174 . AI can also do end-to-end detection without using additional diagnostic methods.…”

Section: Integrating Sensory Change Detection With Artificial Intelli...mentioning

confidence: 99%

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Early Sensory Deficits in Alzheimer’s Disease: A Review of Potential Integrating Diagnostic Methods

Yang

2024

J. Clin. Trans. Sci.

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Section: The Pathogenesis Of Admentioning

confidence: 99%

Section: The Pathogenesis Of Admentioning

confidence: 99%

Section: Integrating Sensory Change Detection With Artificial Intelli...mentioning

confidence: 99%

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Early Sensory Deficits in Alzheimer’s Disease: A Review of Potential Integrating Diagnostic Methods

Yang

2024

J. Clin. Trans. Sci.

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“…With the development of science and technology, many technologies such as biomarker research ( Singhal et al, 2023 ), neuroimaging technology ( Aberathne et al, 2023 ; Castaldo et al, 2023 ), artificial intelligence and machine learning ( Rahaman et al, 2020 , 2021 ; Chen A. et al, 2022 ; Inoue et al, 2023 ), and remote monitoring and sensor technology ( Lu et al, 2023 ; Rahman et al, 2023 ) have been widely used to explore various diseases and achieve interesting results. Machine learning discussed in this article has been the hottest topic in the field of artificial intelligence in the past 10 years, among which deep learning, classification, neural networks, etc.…”

Section: Current Status and Future Prospectsmentioning

confidence: 99%

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Li,

Zhang,

Chen

et al. 2023

Front. Hum. Neurosci.

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Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

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Enhanced Detection of Alzheimer’s and Mild Cognitive Impairment: Leveraging Advanced Preprocessing and Convolutional Neural Networks

Pandey,

Pruthi,

Khan

2024

Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security

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Detection of Alzheimer’s disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

Cited by 15 publications

References 84 publications

Early Sensory Deficits in Alzheimer’s Disease: A Review of Potential Integrating Diagnostic Methods

Early Sensory Deficits in Alzheimer’s Disease: A Review of Potential Integrating Diagnostic Methods

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Enhanced Detection of Alzheimer’s and Mild Cognitive Impairment: Leveraging Advanced Preprocessing and Convolutional Neural Networks

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