Interoperable multi-modal data analysis platform for Alzheimer's disease management

Neurological diseases are generating a wealth of data that can provide valuable insights into disease prediction and auxiliary diagnosis. There is a lack of specialized and standardized disease data analysis platforms that provide the technical approach to support the entire data analysis, namely data selection, management, analysis, visualization and sharing. This paper introduces a platform for the display and analysis of neurological research data, with a focus on Alzheimer's disease (AD), and presents the technical architecture of the AD data analysis platform. The platform provides a technical solution for analyzing AD data from multiple sources, thereby increasing the utilization and value of multi-modal data. A key aspect is the annotation and interpretation of medical raw data, as well as statistical analysis and visualization of MRI data. More importantly, another aspect is to classify through algorithm analysis to effectively predict the disease, so as to achieve the effect of auxiliary diagnosis. We use the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to train and test the classifier, as well as annotate and display the private data about AD obtained from the neurology department of the hospital. The design of the proposed network platform is extensible and can be easily adapted to other neurological diseases.