AI Medical Compendium Topic:
Alzheimer Disease

Clear Filters Showing 511 to 520 of 897 articles

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Medical image analysis
Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonl...

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

NeuroImage. Clinical
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We arg...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Medical image analysis
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. Th...

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

International journal of computer assisted radiology and surgery
PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensitive lesions can provide a visual basis for the diagnosis results. This study proposes an improved end...

Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the multi-modality based methods usuall...

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

NeuroImage
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accura...

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

NeuroImage
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional meas...

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clini...

The NAD-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications.

Mechanisms of ageing and development
Nicotinamide adenine dinucleotide (NAD) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD depletion is associa...