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Dementia

Dementia

Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test.

Akira Masuo^1,2,3, Junpei Kubota⁴, Katsuhiko Yokoyama⁴

¹Seijoh University, Aichi, Japan.

The Clinical Neuropsychologist

|October 22, 2024

Summary

Early dementia diagnosis is vital. This study identified key clock drawing test (CDT) features, like number placement, to effectively screen for dementia in older adults, achieving 74% accuracy.

Keywords:

Alzheimer’s disease clock drawing test dementia screening machine learning neuropsychological testing

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Geriatric Medicine
Neurology
Artificial Intelligence in Healthcare

Background:

Early diagnosis of dementia is critical for effective geriatric care and intervention.
Clock Drawing Tests (CDT) offer a potential non-invasive method for dementia screening.
Identifying specific CDT features predictive of dementia can enhance diagnostic accuracy.

Purpose of the Study:

To develop and evaluate a dementia screening model utilizing drawing features from the Clock Drawing Test (CDT).
To identify specific CDT drawing features that discriminate between individuals with and without dementia.
To assess the screening performance of the developed model in a clinical setting.

Main Methods:

Analysis of 129 older adults from a dementia outpatient clinic, categorized into dementia (n=58) and non-dementia (n=71) groups.
Quantification of 12 drawing features from CDT using the Freedman scoring system.
Application of Boruta feature selection and Support Vector Machine (SVM) for discrimination analysis.

Main Results:

Five CDT drawing features, including 'numbers in the correct position' and 'hand in correct proportion,' were identified as significant discriminators.
The selected features demonstrated a diagnostic sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for dementia detection.
The machine learning model effectively differentiated between dementia and non-dementia groups based on CDT features.

Conclusions:

Drawing features from CDT can be effectively used to identify individuals likely to have dementia in a clinical setting.
Understanding these specific drawing features aids clinical reasoning and offers new insights for dementia diagnosis.
Future research aims to develop a primary dementia screening tool leveraging machine learning and CDT data.