Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …
[HTML][HTML] Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions
Abstract Background & Objective With populations aging, the number of people with
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach
VS Diogo, HA Ferreira, D Prata… - Alzheimer's Research & …, 2022 - Springer
Background Early and accurate diagnosis of Alzheimer's disease (AD) is essential for
disease management and therapeutic choices that can delay disease progression. Machine …
disease management and therapeutic choices that can delay disease progression. Machine …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Studying the manifold structure of Alzheimer's disease: a deep learning approach using convolutional autoencoders
Many classical machine learning techniques have been used to explore Alzheimer's
disease (AD), evolving from image decomposition techniques such as principal component …
disease (AD), evolving from image decomposition techniques such as principal component …
Quantifying performance of machine learning methods for neuroimaging data
Abstract Machine learning is increasingly being applied to neuroimaging data. However,
most machine learning algorithms have not been designed to accommodate neuroimaging …
most machine learning algorithms have not been designed to accommodate neuroimaging …
Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects
Dementia, a chronic and progressive cognitive declination of brain function caused by
disease or impairment, is becoming more prevalent due to the aging population. A major …
disease or impairment, is becoming more prevalent due to the aging population. A major …
Alzheimer's disease progression detection model based on an early fusion of cost-effective multimodal data
Alzheimer's disease (AD) is a severe neurodegenerative disease. The identification of
patients at high risk of conversion from mild cognitive impairment to AD via earlier close …
patients at high risk of conversion from mild cognitive impairment to AD via earlier close …
Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …