An Interpretable Framework for Identifying Cerebral Microbleeds and Alzheimer's Disease Severity using Multimodal
Abstract:
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.
Insights
This study introduces explainable AI methods to reliably detect cerebral microbleeds (CMBs) in MRI images and assess Alzheimer's disease (AD) severity using gene expression data, improving early diagnosis.
Area of Science:
- Neuroimaging and computational biology
- Artificial intelligence in medicine
- Biomarker discovery for neurodegenerative diseases
Background:
- Cerebral microbleeds (CMBs) are crucial indicators for cerebrovascular disorders like stroke and Alzheimer's disease (AD).
- Automating CMB detection and enhancing prediction reliability for early chronic disease diagnosis remains a significant challenge.
- Existing methods using spike neural networks (SNNs) and decision trees lack interpretability.
Purpose of the Study:
- To develop an explainable artificial intelligence (XAI) system for automated detection of CMBs in MRI images.
- To determine Alzheimer's disease (AD) severity using gene expression data.
- To enhance the reliability and interpretability of diagnostic and prognostic predictions.
Main Methods:
- Utilized a spike neural network (SNN) for CMB identification in MRI images.
- Employed a decision tree for analyzing gene expression data to assess AD severity.
- Developed and applied Pixel Density Analysis (PDA) for MRI data interpretation.
- Implemented Probabilistic Graphical Models (PGM) for gene expression data interpretation.
Main Results:
- Successfully identified cerebral microbleeds (CMBs) in MRI scans.
- Determined the severity of Alzheimer's disease (AD) through gene expression analysis.
- Achieved enhanced reliability in prediction outputs via explainable AI (XAI) methods.
- Provided interpretable insights into diagnostic and prognostic decision-making processes.
Conclusions:
- Explainable AI (XAI) methods, specifically PDA and PGM, significantly improve the reliability of CMB detection and AD severity assessment.
- The developed XAI system offers interpretable outputs, addressing the limitations of complex 'black-box' models.
- This approach holds promise for earlier and more accurate diagnosis and management of neurodegenerative conditions.
