MRI and Language Based Dementia Evaluation and Risk Scoring

Overview

This project combines brain MRI imaging analysis with natural language processing to create a multimodal approach for evaluating dementia risk and enabling early detection. By leveraging both structural brain data and linguistic patterns, the system provides a more comprehensive assessment than single-modality approaches.

The Problem

Dementia affects millions of people worldwide, and early detection is crucial for better outcomes. Traditional diagnostic methods often rely on a single type of assessment. This project explores how combining multiple data sources—specifically brain imaging and language analysis—can improve detection accuracy and provide earlier warnings of cognitive decline.

Approach

• MRI Analysis: Using deep learning models to analyze structural brain MRI scans for patterns associated with dementia and cognitive decline.
• Language Analysis: Applying natural language processing techniques to identify linguistic markers that may indicate early-stage cognitive impairment.
• Multimodal Fusion: Combining insights from both modalities to generate a comprehensive risk score.

Technical Details

The project is built using PyTorch for deep learning model development, with specialized architectures for processing medical imaging data. The NLP component uses transformer-based models to analyze speech and text patterns for subtle changes in language use that may indicate cognitive changes.

Status

This is an ongoing research project. The goal is to develop a tool that could assist healthcare professionals in early screening and risk assessment for dementia.