[HTML][HTML] Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early …

K Shanmugavadivel, VE Sathishkumar, J Cho… - Ageing Research …, 2023 - Elsevier
Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually
causes brain cells to die. This neurological condition progressively hampers cognitive and …

Artificial intelligence techniques for automated diagnosis of neurological disorders

U Raghavendra, UR Acharya, H Adeli - European neurology, 2020 - karger.com
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …

Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis

T Zhou, KH Thung, X Zhu, D Shen - Human brain map**, 2019 - Wiley Online Library
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …

An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis

Q Li, H Chen, H Huang, X Zhao, ZN Cai… - … methods in medicine, 2017 - Wiley Online Library
In this study, a new predictive framework is proposed by integrating an improved grey wolf
optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO‐KELM …

Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression

N Koutsouleris, DB Dwyer, F Degenhardt, C Maj… - JAMA …, 2021 - jamanetwork.com
Importance Diverse models have been developed to predict psychosis in patients with
clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining …

Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for …

PM Rossini, F Miraglia, F Vecchio - Alzheimer's & Dementia, 2022 - Wiley Online Library
Introduction Dementia in its various forms represents one of the most frightening
emergencies for the aging population. Cognitive decline—including Alzheimer's disease …

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2015 - Elsevier
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing,
longitudinal, multicenter study designed to develop clinical, imaging, genetic, and …

[PDF][PDF] A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19)

AA Farid, GI Selim, HAA Khater - Preprints, 2020 - academia.edu
The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic
model. It involves a technique for classification and prediction by recognizing typical and …

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Z Li, X Jiang, Y Wang, Y Kim - Emerging topics in life sciences, 2021 - portlandpress.com
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few
preventive or curative treatments available. Modern technology developments of high …

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease

K Blanco, S Salcidua, P Orellana… - Alzheimer's Research & …, 2023 - Springer
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with
estimated rates of progression to dementia up to 80–90% after approximately 6 years from …