Objective The main objective is to develop a Content-Based Image Retrieval (CBIR) system using a large database of longitudinal brain Magnetic Resonance Imaging (MRI) of patients with dementia. By using artificial intelligence, the system will detect patterns and similarities in the longitudinal images, empowering healthcare professionals to predict treatment outcomes and deliver personalized care. Eventually, this tool aims to simplify decision making, improve patient care and make healthcare more efficient and cost-effective. Although the initial storage estimate is approximately 450 GB for 30,000 images, the total storage capacity required can be expected to grow significantly due to the outputs generated by the various experiments to be performed. In particular, the storage could increase to 1000 GB and the total number of files could increase by up to a factor of 5. Moreover, storing non-compressed versions of the data is essential to ensure fast read operations, which in turn optimizes the use of computational resources—for example, by reducing data loading time during deep learning model training, thus improving GPU utilization. Background As the global population ages, dementia diseases are becoming increasingly prevalent, currently affecting approximately 47 million individuals and imposing an economic burden of around $2.8 trillion. Innovative computer-aided diagnosis techniques, particularly CBIR, have been transformed by enhancing the retrieval of relevant images for patients with or at risk of dementia. Scientific evidence suggests that spatio-temporal patterns from longitudinal recordings can significantly improve outcomes in cross-sectional studies. However, progress in this field has been hindered by limited access to longitudinal databases, small dataset sizes, and ineffective analytical methods.