Alzheimer's Disease Detection Techniques: A Review

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia worldwide. Conventional diagnostic methods, including cerebrospinal fluid analysis and clinical evaluation, are invasive and typically effective only at later stages, despite the importance of early detection for therapeutic success. Advances in neuroimaging, particularly magnetic resonance imaging (MRI), coupled with machine learning (ML) and deep learning (DL), have enabled non-invasive identification of structural and functional brain alterations characteristic of AD. Convolutional neural networks (CNNs) and long short-term memory (LSTM) models are particularly effective for processing large-scale volumetric and temporal data. Incorporating genetic and clinical biomarkers alongside imaging data further enhances diagnostic performance. Nonetheless, significant challenges remain, including limited datasets, heterogeneous sources of information, algorithm interpretability, and ethical considerations. Emerging solutions such as federated learning, standardized preprocessing pipelines, and explainable AI (XAI) frameworks aim to address these barriers and improve the reliability of computational models in clinical settings. This review discusses recent progress in AI-driven approaches for AD detection and underscores the importance of interdisciplinary strategies to facilitate earlier diagnosis, improve patient outcomes, and support more effective management of Alzheimer’s disease.