ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction

ADInsight represents the crux of this dissertation, introducing an integrated and explainable framework centered on predicting Alzheimer's disease (AD) conversion, particularly for those at the early stage of mild cognitive impairment (EMCI). Beginning with an examination of models grounded in individual research modalities, such as clinical data and advanced imaging, the research underscores the potential and limitations of singular approaches. As a response to these findings, this dissertation introduces a multimodal ensemble conversion prediction model that combines Diffusion Tensor Imaging (DTI) scans with clinical data. This ensemble not only increases the accuracy of predictions but is also notable for its dedication to explainability, bridging the gap between intricate neural network predictions and understandable medical interpretations. Upon further exploration a unique framework is revealed, combining the advantages of Random Forest Regression alongside the latest over-sampling methods. This framework unravels the intricacies of AD's nonlinear progression, leading to the formulation of patient progression groupings. The dissertation is then concluded with the Cognitive Visual Recognition Tracker (CVRT) application. This application marks an exploration into cognitive focus and visual identification, which are essential elements in the development of Alzheimer's disease. Benefiting both clinicians and patients, CVRT paves the way for innovative treatment strategies. In summary, our ADInsight framework provides a novel approach to understanding and predicting the progression of AD, providing a beacon of hope and knowledge in the ongoing struggle against this debilitating condition.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Biomedical and Health Informatics (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2023

Author dc:creator

• Velazquez, Matthew J.

Advisors dc:contributor.advisor

• Lee, Yugyung, 1960-
• Gaddis, Monica Louise, 1955-

Handle dc:identifier.uri

https://hdl.handle.net/10355/98063

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/98063