Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

S Mirkin, BC Albensi - Frontiers in Aging Neuroscience, 2023 - frontiersin.org
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory,
thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD …

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease

C Wang, H Tachimori, H Yamaguchi… - Translational …, 2024 - nature.com
Alzheimer's disease is one of the most important health-care challenges in the world. For
decades, numerous efforts have been made to develop therapeutics for Alzheimer's …

[HTML][HTML] A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer's disease

P Lu, L Hu, A Mitelpunkt, S Bhatnagar, L Lu… - … Signal Processing and …, 2024 - Elsevier
Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of
the most common neurodegenerative diseases. Recent studies have demonstrated the …

A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer's Disease involving data synthesis

K Chen, Y Weng, AA Hosseini, T Dening, G Zuo… - Neural Networks, 2024 - Elsevier
Alzheimer's Disease (AD) is a neurodegenerative disease that commonly occurs in older
people. It is characterized by both cognitive and functional impairment. However, as AD has …

Vision transformer approach for classification of Alzheimer's disease using 18F-Florbetaben brain images

H Shin, S Jeon, Y Seol, S Kim, D Kang - Applied Sciences, 2023 - mdpi.com
Dementia is a degenerative disease that is increasingly prevalent in an aging society.
Alzheimer's disease (AD), the most common type of dementia, is best mitigated via early …

[HTML][HTML] Applications of deep learning for drug discovery systems with bigdata

Y Matsuzaka, R Yashiro - BioMedInformatics, 2022 - mdpi.com
The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

Himal: Multimodal hi erarchical m ulti-task a uxiliary l earning framework for predicting alzheimer's disease progression

S Kumar, SC Yu, A Michelson, T Kannampallil… - JAMIA …, 2024 - academic.oup.com
Objective We aimed to develop and validate a novel multimodal framework Hi erarchical M
ulti-task A uxiliary L earning (HiMAL) framework, for predicting cognitive composite functions …

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study

H Jiang, P Chen, Z Sun, C Liang, R Xue… - …, 2023 - nature.com
Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At
present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to …

Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models

S Yamada, H Ito, H Matsumasa, S Ii, T Otani… - Frontiers in Aging …, 2024 - frontiersin.org
Background Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a
key feature for Hakim disease (idiopathic normal pressure hydrocephalus: iNPH), but …

Effect and Potential Mechanism of Immunotherapy on Cognitive Deficits in Animal Models of Alzheimer's Disease: A Systematic Review and Meta-Analysis

Z Zhai, F Kong, Z Zhu, J Dai, J Cai, D **e… - The American Journal of …, 2024 - Elsevier
Objective Immunotherapy has been reported to ameliorate Alzheimer's disease (AD) in the
animal model; however, the immunologic approaches and mechanisms have not been …