AIMC Topic: Alzheimer Disease
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A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 22, 2025
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...
Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.
American journal of Alzheimer's disease and other dementias
Apr 21, 2025
A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability and relative importance of the most influential factors remain underexplored. We compiled various w...
Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.
Epilepsy & behavior : E&B
Apr 17, 2025
BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we levera...
Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.
The journal of prevention of Alzheimer's disease
Apr 16, 2025
BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment.
Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.
ACS nano
Apr 15, 2025
Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpre...
c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.
PloS one
Apr 14, 2025
Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently available diagnostic biomarkers preclude early diagnosis, necessitating the development of alternative bio...
Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.
Scientific reports
Apr 13, 2025
In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (M...
Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers.
Molecular psychiatry
Apr 12, 2025
Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer's disease (AD) and other neurodegenerative diseases. However, no previous studies ha...
The future of Alzheimer's disease risk prediction: a systematic review.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Apr 12, 2025
BACKGROUND: Alzheimer's disease is the most prevalent kind of age-associated dementia among older adults globally. Traditional diagnostic models for predicting Alzheimer's disease risks primarily rely on demographic and clinical data to develop polic...
Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.
International journal of medical informatics
Apr 11, 2025
OBJECTIVES: Unstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate i...
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