This study introduces two classification models to discriminate whether the presumptive etiological cause of cognitive impairment is Alzheimer's disease, and demonstrates diagnostic performance comparable, and slightly superior, to that of clinicians when evaluated under similar informative constraints.
This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs and particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and...
Xia-Yao Guo, Yan-Qi Sun, Yang Chen et al.· Biosensors· 0 citations
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.
A. Seçkin, Fatih Soygazi· Neural computing & applicati...· 0 citations
Identifying progressive dementia during its prodromal stage is a challenging yet critical task for healthcare providers. Traditional clinical assessments are often not sensitive enough to detect warning signs of cognitive decline. Machine learning (ML) methods have shown promise in modeling clinical trajectories associ...
A. Gautier, L. Zheng, B. Schreurs· medRxiv· 0 citations
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...
Nuerbiya Keranmu, Dilireba Aizezi, Xing-Yong Pan et al.· Frontiers in Aging Neuroscie...· 0 citations
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.
K. V. S. V. Trinadh Reddy, Gundeboyina Srinivasalu, T. V. Rao et al.· Archives of Medical Research· 0 citations
Alzheimer’s disease has been difficult to diagnose early because structural changes in the brain often appear subtle and overlap with normal aging. This study aimed to improve the accuracy and interpretability of Alzheimer’s disease detection from structural magnetic resonance imaging by developing a classification fra...
Kelvin Leonardi Kohsasih, Robet Robet, Johanes Terang Kita Perangin Angin et al.· Jurnal Informatika· 0 citations
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