Dementia Detection

Dementia detection research aims to develop accurate and accessible diagnostic methods for early identification of cognitive decline, improving patient outcomes and reducing healthcare burdens. Current efforts heavily utilize machine learning, particularly deep learning models like convolutional neural networks and transformers, analyzing multimodal data including speech, neuroimaging (MRI, PET), and genetic information to classify dementia subtypes and stages. These advancements leverage various techniques such as multimodal fusion, data augmentation, and explainable AI to enhance diagnostic accuracy and interpretability, although challenges remain in generalizability and clinical integration. The ultimate goal is to translate these research findings into robust, reliable, and cost-effective clinical tools for early dementia detection and personalized management.