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Dementia screening with the Rivermead Behavioural Memory Test: A machine learning analysis.
Yu Fujiwara1, Katsutaka Toyoda2, Soichiro Maruyama2
1Department of Psychiatry, National Defense Medical College, School of Medicine, Tokorozawa, Saitama, Japan.
Journal of Alzheimer'S Disease : JAD
|April 25, 2026
Summary
The Rivermead Behavioural Memory Test (RBMT) significantly aids in diagnosing dementia and mild cognitive impairment (MCI), outperforming standard screening tools like MMSE and MoCA-J, especially for borderline cases.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Gerontology
Background:
- Early identification of Alzheimer's disease and related dementias is crucial due to new therapies and the increasing societal burden of dementia.
- Developing accurate diagnostic tools for early detection of cognitive decline is a clinical priority.
Purpose of the Study:
- To assess the diagnostic accuracy of a machine learning model utilizing a neuropsychological battery for classifying individuals into Healthy controls, mild cognitive impairment (MCI), or Dementia categories.
- To identify key neuropsychological tests and cognitive domains that most influence classification accuracy, aiming to determine optimal dementia screening tools.
Main Methods:
- A retrospective, cross-sectional study analyzed 590 participants evaluated for suspected dementia.
- A random forest machine learning model was trained using scores from the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment Japanese version (MoCA-J), Rivermead Behavioural Memory Test (RBMT), Japanese Adult Reading Test (JART), and Wechsler Adult Intelligence Scale-III.
- Model performance was evaluated using the area under the ROC curve (AUC), and variable importance analysis identified the contribution of each test.
Main Results:
- The multiclass machine learning model achieved a high diagnostic accuracy with an AUC of 0.898.
- The Rivermead Behavioural Memory Test (RBMT) demonstrated the strongest contribution to classification, surpassing the MMSE and MoCA-J.
- In participants with borderline MMSE/MoCA-J scores, incorporating RBMT significantly improved classification accuracy for both Healthy vs. MCI and MCI vs. Dementia distinctions.
Conclusions:
- The RBMT offers significant added value in differentiating dementia and MCI, particularly as a secondary assessment for borderline screening results.
- While effective, the RBMT's administration time may restrict its use as a universal first-line dementia screening tool.
Related Concept Videos
01:30Dementia
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.
The progression of dementia is generally gradual.
01:22Dementia l: Introduction
Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...