AIMC Topic: Alzheimer Disease
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Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.
Journal of neuroscience methods
Feb 24, 2018
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...
Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.
Medical image analysis
Feb 21, 2018
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care...
Higher Serum Endocan Level Is Associated with Alzheimer Disease.
Dementia and geriatric cognitive disorders
Jan 26, 2018
BACKGROUND: The novel molecule endocan, which is released by endothelium and is regulated by proangiogenic and proinflammatory cytokines, may have a role in the pathophysiology of Alzheimer disease (AD). The aim of this study was to evaluate the rela...
Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.
Translational research : the journal of laboratory and clinical medicine
Jan 10, 2018
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus ...
Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.
BMC neurology
Jan 10, 2018
BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes i...
A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease.
Journal of neuroscience methods
Jan 9, 2018
BACKGROUND: AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in the management of AD patients.
Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt.
Journal of neuroscience methods
Jan 2, 2018
Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.
Frontiers in bioscience (Landmark edition)
Jan 1, 2018
Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing A...
Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.
Journal of neuroscience methods
Dec 26, 2017
BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challen...
Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.
Journal of neuroscience methods
Dec 18, 2017
BACKGROUND: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no stud...
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