TL;DR
The results suggest that combining EEG data with clinical and demographic information can improve the accuracy of AD and FTD diagnosis, providing a promising tool for early detection.
Objective Alzheimer's disease (AD), the most common neurodegenerative disorder, is a leading cause of cognitive impairment and dementia in older adults. This study aimed to develop an interpretable machine learning model using multimodal MRI radiomics for the diagnosis of Alzheimer's disease. Materials and methods A to...
This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020 and 2025, with a primary focus on Alzheimer’s disease, frontotemporal dementia (FTD), mild cognitive impairment (MCI), and related dementia disorders.
It is suggested that resting-state EEG can provide non-invasive and interpretable information about AD-related functional alterations, pending validation in larger, independent cohorts.
A domain-informed heterogeneous ensemble framework incorporating dynamic neural reactivity and hemispheric asymmetry metrics from 19-channel EEG recordings acquired from 88 participants indicates that the integration of dynamic state-transition measures with structural asymmetry proxies enhances electrophysiological di...
The findings support the potential role of structural MRI biomarkers combined with interpretable ML approaches for the classification of cognitive impairment and the integration of demographic variables improved model performance, particularly in binary discrimination tasks, whereas multiclass classification remained m...
An advanced DL-powered framework for the early prediction of AD using MRI data demonstrates high accuracy, robustness, and interpretability, supporting its potential for reliable early AD diagnosis in clinical settings.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
