The use of neuroimaging techniques in the early and differential diagnosis of dementia

L Chouliaras, JT O'Brien - Molecular Psychiatry, 2023 - nature.com
Dementia is a leading cause of disability and death worldwide. At present there is no
disease modifying treatment for any of the most common types of dementia such as …

[HTML][HTML] A review of the application of deep learning in the detection of Alzheimer's disease

S Gao, D Lima - International Journal of Cognitive Computing in …, 2022 - Elsevier
Alzheimer's disease (AD) is the most common chronic disease in the elderly, with a high
incidence rate. In recent years, deep learning has become popular in the field of medical …

Machine learning and novel biomarkers for the diagnosis of Alzheimer's disease

CH Chang, CH Lin, HY Lane - International journal of molecular sciences, 2021 - mdpi.com
Background: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease
that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on …

[HTML][HTML] Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions

R Li, X Wang, K Lawler, S Garg, Q Bai, J Alty - Journal of biomedical …, 2022 - Elsevier
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 …

Application of Artificial Intelligence techniques for the detection of Alzheimer's disease using structural MRI images

X Zhao, CKE Ang, UR Acharya, KH Cheong - … and Biomedical Engineering, 2021 - Elsevier
Alzheimer's disease (AD) is an irreversible, progressive brain disorder that slowly destroys
memory and thinking skills. It is one of the leading types of dementia for persons aged above …

Generative adversarial network constrained multiple loss autoencoder: A deep learning‐based individual atrophy detection for Alzheimer's disease and mild cognitive …

R Shi, C Sheng, S Jin, Q Zhang, S Zhang… - Human brain …, 2023 - Wiley Online Library
Exploring individual brain atrophy patterns is of great value in precision medicine for
Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current …

AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling

MB Kale, NL Wankhede, RS Pawar, S Ballal… - Ageing Research …, 2024 - Elsevier
Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research
and clinical practice due to its complex etiology and progressive nature. The integration of …

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

AS Tang, KP Rankin, G Cerono, S Miramontes, H Mills… - Nature Aging, 2024 - nature.com
Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before
irreversible disease progression. We demonstrate that electronic health records from the …

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease

M Inglese, N Patel, K Linton-Reid, F Loreto… - Communications …, 2022 - nature.com
Background Alzheimer's disease, the most common cause of dementia, causes a
progressive and irreversible deterioration of cognition that can sometimes be difficult to …

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach

AM Gaeta, M Quijada-López, F Barbé… - Frontiers in Aging …, 2024 - frontiersin.org
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current
core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a …