Automated Evaluation of Standardized Dementia Screening Tests

TL;DR

This study explores automated scoring of dementia screening tests using speech transcripts, demonstrating high correlation with expert evaluations and highlighting the impact of transcription accuracy on scoring reliability.

Contribution

The paper presents a novel approach to automatically evaluate cognitive test performance from speech transcripts, improving scoring accuracy and reliability in dementia assessments.

Findings

High correlation between manual and automatic scoring with manual transcripts

Automatic scoring accuracy depends on transcription quality

Word alternatives improve automatic scoring correlation

Abstract

For dementia screening and monitoring, standardized tests play a key role in clinical routine since they aim at minimizing subjectivity by measuring performance on a variety of cognitive tasks. In this paper, we report on a study that consists of a semi-standardized history taking followed by two standardized neuropsychological tests, namely the SKT and the CERAD-NB. The tests include basic tasks such as naming objects, learning word lists, but also widely used tools such as the MMSE. Most of the tasks are performed verbally and should thus be suitable for automated scoring based on transcripts. For the first batch of 30 patients, we analyze the correlation between expert manual evaluations and automatic evaluations based on manual and automatic transcriptions. For both SKT and CERAD-NB, we observe high to perfect correlations using manual transcripts; for certain tasks with lower correlation, the automatic scoring is stricter than the human reference since it is limited to the audio. Using automatic transcriptions, correlations drop as expected and are related to recognition accuracy; however, we still observe high correlations of up to 0.98 (SKT) and 0.85 (CERAD-NB). We show that using word alternatives helps to mitigate recognition errors and subsequently improves correlation with expert scores.