A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts

TL;DR

This paper presents a visual analytics system that leverages AI and machine learning to analyze DTI fiber tract data for studying neurodegenerative diseases, aiding early diagnosis and understanding of disease effects on the brain.

Contribution

It introduces an AI-augmented visual analytics platform that integrates machine learning with linked visualizations for comprehensive analysis of DTI data in neurodegenerative disease research.

Findings

Effective analysis of Parkinson's data demonstrated system's utility.

Machine learning pipeline helps identify relevant features.

Visualizations facilitate understanding of disease-related neural changes.

Abstract

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce an intelligent visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's AI-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space, and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using real data from the research database of Parkinson's Progression Markers Initiative.