Deep Learning for Dementia Disease Prediction Using the ADNI Dataset: A Comprehensive Review

Alzheimer's disease (AD) and dementia in general constitute one of the key neurological problems in the current twenty-first century and currently affect more than 55 million people worldwide. Early and accurate diagnostics still poses a great challenge, which demands the use of automated and data-driven computational methods. In the last decade, deep learning models became the key instrument for predicting dementia utilizing the vast array of neuroimaging and multi-modal biomarkers provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). This literature review presents an overview of different deep learning approaches used for predicting dementia based on the ADNI dataset in 2011-2024. Classical machine learning approaches, convolutional neural networks (CNNs), recurrent networks (RNNs, LSTMs), transformers, graph neural networks (GNNs), and generative adversarial networks (GANs) are considered. Relevant aspects such as the type of input, classification task, architecture, and associated performance measures are discussed and reviewed. Important issues include data paucity, site diversity, class imbalance, model explainability, and obstacles to clinical translation. Novel directions such as federated learning, explainable AI (XAI), foundation models, and multimodal integration are highlighted. The review is intended as a systematic guide for navigating the fast-developing field of artificial intelligence-assisted dementia detection.