A tool to support the diagnosis of Alzheimer’s disease

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder affecting millions of individuals worldwide. Currently, there is no cure, making early recognition critical, as timely interventions can help slow functional deterioration and maintain quality of life. This dissertation aimed to develop a tool to support the diagnosis of Alzheimer’s disease. The tool is designed to complement existing assessment methods rather than replace them, supporting practitioners in their diagnostic process. The objectives were achieved by developing a Convolutional Neural Network (CNN) model to classify MRI scans into four stages of Alzheimer’s disease, creating a prototype web application to evaluate whether the integration of the model with it is feasible, and conducting interviews with domain experts to inform the tool’s features and functionalities. The prototype was then refined and re-evaluated with expert feedback. The resulting web application allows authorised medical specialists to log in, manage patient information, upload MRI scans, predict the stage of Alzheimer’s disease, and access comprehensive reports that include both current and past scans for comparison. This tool demonstrates the potential of integrating artificial intelligent assisted imaging analysis into clinical workflows to support more informed and efficient diagnostic decisions. Domain experts evaluated the tool as aesthetically pleasing, easy to follow, clear, and straightforward to use.