An interpretable framework for identifying cerebral microbleeds and Alzheimer’s disease severity using multimodal data

Cerebral microbleeds (CMBs) are tiny chronic brain haemorrhages that have been recognised as prognostic indicators for a number of acute cerebrovascular disorders, such as stroke, traumatic disorder, and Alzheimer’s disease. For early-stage chronic disease diagnosis, it is challenging to automate the detection of CMBs and increase the reliability of prediction outputs. This study developed a system for identifying microbleeds in MRI images and gene expression data and determining the severity of Alzheimer’s disease (AD). Initially, a spike neural network (SNN) and decision tree were utilised to identify microbleeds in AD from MRI images and gene expression respectively. However, the conclusions of these two methods cannot be interpreted due to the complexity of their internal processing steps. This study proposed two explainable artificial intelligence (XAI) methods for interpreting prediction outputs in an effort to boost reliability. Pixel density analysis (PDA) and probabilistic graphical model (PGM) explain the decision-making processes for MRI images and gene expression data for the diagnosis of microbleeds and the severity analysis of AD.