Dementia screening with the Rivermead Behavioural Memory Test: A machine learning analysis

Abstract

Background

With the advent of anti-amyloid-β monoclonal antibody therapies and the growing societal burden of dementia, early identification of Alzheimer's disease and related dementias has become a clinical priority.

Objective

To evaluate the diagnostic accuracy of a machine learning model using a neuropsychological battery to classify individuals as Healthy controls, mild cognitive impairment (MCI), or Dementia, and to identify neuropsychological tests and cognitive domains that contributed most to classification accuracy, determining optimal tests for dementia screening.

Methods

In this retrospective cross-sectional single-center study, we analyzed 590 participants evaluated for suspected dementia. The final sample comprised 74 Healthy controls, 190 individuals with MCI, and 326 with Dementia (including 269 with Alzheimer's disease). 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 were input into a random forest machine learning model. Model performance was assessed using the area under the ROC curve (AUC). A variable importance analysis determined each test's relative contribution to classification.

Results

The multiclass model achieved an AUC of 0.898. RBMT was the strongest contributor, exceeding MMSE and MoCA-J. In borderline MMSE/MoCA-J subsets, adding RBMT improved classification performance for both Healthy versus MCI and MCI versus Dementia decisions.

Conclusions

RBMT provides substantial incremental value for dementia-related diagnostic discrimination, particularly as a second-line assessment when brief screening results are borderline. However, its administration time may limit its role as a universal first-line screening tool.