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Related Experiment Video

Updated: Aug 29, 2025

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms.

Chaeyoon Park¹, Jae-Won Jang^1,2,3, Gihun Joo³

¹Department of Convergence Security, Kangwon National University, Chuncheon, South Korea.

Frontiers in Neurology

|September 8, 2022

Summary

This summary is machine-generated.

Predicting dementia progression in mild cognitive impairment (MCI) is vital. Tree-based machine learning models using Comprehensive Visual Rating Scale (CVRS) scores and clinical data accurately forecast dementia development over two years.

Keywords:

Alzheimer's Disease brain MRI machine learning mild cognition impairment visual rating scale

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Area of Science:

Neuroimaging and Machine Learning
Biomarker Discovery
Cognitive Decline Research

Background:

Identifying reliable biomarkers for predicting dementia progression in mild cognitive impairment (MCI) is a critical clinical need.
The Comprehensive Visual Rating Scale (CVRS), derived from magnetic resonance imaging (MRI), assesses brain structural changes in MCI patients.
This study explores the predictive utility of CVRS scores for dementia onset in MCI patients.

Purpose of the Study:

To evaluate the effectiveness of the Comprehensive Visual Rating Scale (CVRS) in predicting the progression of mild cognitive impairment (MCI) to dementia.
To compare the performance of various machine learning (ML) algorithms in forecasting dementia development.
To identify key predictors of MCI to dementia conversion using baseline data.

Main Methods:

Utilized data from 197 MCI patients from the Japanese-Alzheimer's Disease Neuroimaging Initiative study with over two years of follow-up.
Assessed patients using Comprehensive Visual Rating Scale (CVRS) scores, cortical thickness, and clinical data.
Applied machine learning algorithms including logistic regression, Random Forest (RF), XGBoost, and LightGBM for prediction and feature importance analysis.

Main Results:

Out of 197 MCI patients, 108 (54.8%) progressed to dementia within the follow-up period.
Tree-based classifiers (XGBoost, LightGBM, RF) demonstrated superior performance in predicting dementia progression.
Models combining clinical data and CVRS scores achieved higher accuracy (0.701-0.711) compared to those using clinical data and cortical thickness (0.650-0.685).

Conclusions:

Tree-based machine learning algorithms effectively predict MCI to dementia progression.
Baseline Comprehensive Visual Rating Scale (CVRS) scores, when combined with clinical data, serve as valuable predictors.
This approach offers a promising method for early identification of individuals at high risk of dementia conversion.