Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction

TL;DR

This paper reviews existing models for Alzheimer's disease prediction, emphasizing the importance of multimodal and temporal data, and proposes a new robust detection model to improve early diagnosis and progression tracking.

Contribution

It introduces a comprehensive comparison of current models and presents a novel, robust detection model that incorporates multimodal and temporal data for better Alzheimer's diagnosis.

Findings

Comparison of various Alzheimer's prediction models

Highlighting the importance of multimodal and temporal data

Proposed a new robust detection model

Abstract

Recent studies on modelling the progression of Alzheimer's disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients' condition at an early stage. This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed.