LURE, FLEMING Y. M.
MS TECHNOLOGIES CORPORATION
Multi-Modality Image Data Fusion and Machine Learning Approaches for Personalized Diagnostics and Prognostics of MCI due to AD
Acquired Cognitive Impairment... Aging... Alzheimer's Disease... Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)... Basic Behavioral and Social Science... Behavioral and Social Science... Bioengineering... Biomedical Informatics Research... Biomedical Information Resources and Informatics Research... Brain Disorders... Clinical Research... Clinical Research - Extramural... Dementia... Diagnostic Radiology... Networking and Information Technology R&D... Neurodegenerative... Neurosciences... Patient Safety... Precision Medicine... Prevention
Alzheimers Disease (AD) is the most common form of dementia and the sixth leading cause of death in the US. More than 5 million people in the US currently have AD and the direct health care cost is over $200 billion per year. Detection of early phase of AD, namely Mild Cognitive Impairment (MCI), can delay, prevent, and treat this serious disease. The project will develop a clinically-feasible system for Mild Cognitive Impairment (MCI) diagnostics and prognostics, by integrating multi-modality imaging data such as MRI and PET as well as non-imaging data such as clinical assessments, biomarkers, demographics, and genetic information. This project involves three Aims. In Aim #1, we will develop the system by designing diagnostic and prognostic modeling using cross-sectionally incomplete multi-modality data by multitask learning. Our multitask-learning approach that will simultaneously model multiple related tasks by allowing effective knowledge and data sharing to jointly estimate the diagnostic/prognostic models for each patient cohort. In Aim #2, we will update diagnostic and prognostic model using longitudinally incomplete multi-modality data by transfer learning. We will integrate the model of an old domain (e.g., the diagnostic/prognostic model obtained at an earlier time point) and the data of a new domain (e.g., new data obtained at the a follow-up visit), in order to obtain an updated model with better accuracy. This can take care of incomplete longitudinal data due to patient drop-off, because it transfers the old- domain model not the data. In Aim #3 we will conduct validation for the proposed models using the MCI data collected by Alzheimer’s Disease Neuroimaging Initiative (ADNI) for all phases of AD. The current project is novel in creating a first-of-its-kind clinically-feasible technology for personalized MCI diagnostics and prognostics as well as in using multitask learning and transfer learning machine learning methods for modeling cross-sectionally and longitudinally incomplete multi-modality data. It is innovative in using multitask learning to model incomplete cross-sectional data (e.g., baseline data) and using transfer learning to model the incomplete longitudinal data.