Artificial Intelligence for Alzheimer's disease diagnosis through T1-weighted MRI: A systematic review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela (USC), Rúa Xosé María Suárez Núñez S/N, 15782, Santiago de Compostela, Spain; Cognitive Neuroscience Research Group (Neucoga-Aging), Instituto de Psicoloxía (IPsiUS), USC, Health Research Institute of Santiago de Compostela (IDIS). Rúa Xosé María Suárez Núñez S/N, 15782, Santiago de Compostela, Spain. Electronic address: [email protected].
- Department of Health Sciences, University of Burgos, Burgos, Spain.
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela (USC), Rúa Xosé María Suárez Núñez S/N, 15782, Santiago de Compostela, Spain; Cognitive Neuroscience Research Group (Neucoga-Aging), Instituto de Psicoloxía (IPsiUS), USC, Health Research Institute of Santiago de Compostela (IDIS). Rúa Xosé María Suárez Núñez S/N, 15782, Santiago de Compostela, Spain.
Abstract
Alzheimer's disease (AD) is a leading cause of dementia worldwide, characterized by heterogeneous neuropathological changes and progressive cognitive decline. Despite the numerous studies, there are still no effective treatments beyond those that aim to slow progression and compensate the impairment. Neuroimaging techniques provide a comprehensive view of brain changes, with magnetic resonance imaging (MRI) playing a key role due to its non-invasive nature and wide availability. The T1-weighted MRI sequence is frequently used due to its prevalence in most MRI protocols, generating large datasets, ideal for artificial intelligence (AI) applications. AI, particularly machine learning (ML) and deep learning (DL) techniques, has been increasingly utilized to model these datasets and classify individuals along the AD continuum. This systematic review evaluates studies using AI to classify more than two stages of AD based on T1-weighted MRI data. Convolutional neural networks (CNNs) are the most widely applied, achieving an average classification accuracy of 85.93 % (range: 51.80-100 %; median: 87.70 %). These good results are due to CNNs' ability to extract hierarchical features directly from raw imaging data, reducing the need for extensive preprocessing. Non-convolutional neural networks and traditional ML approaches also demonstrated strong performance, with mean accuracies of 82.50 % (range: 57.61-99.38 %; median: 86.67 %) and 84.22 % (range: 33-99.10 %; median: 87.75 %), respectively, underscoring importance of input data selection. Despite promising outcomes, challenges remain, including methodological heterogeneity, overfitting risks, and a reliance on the ADNI database, which limits dataset diversity. Addressing these limitations is critical to advancing AI's clinical application for early detection, improved classification, and enhanced patient outcomes.