Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies

TL;DR

This study introduces a machine learning pipeline using insole sensor balance measures to differentiate prodromal dementia with Lewy bodies from other conditions, achieving improved accuracy over traditional methods.

Contribution

The paper presents a novel automatic approach leveraging insole sensor data and machine learning to identify MCI-LB, addressing diagnostic challenges.

Findings

Achieved up to 78.0% accuracy in distinguishing MCI-LB.

Model outperformed demographic and neuropsychological baseline by 6.8%.

Demonstrated potential for early diagnosis of MCI-LB.

Abstract

Dementia with Lewy bodies is the second most common type of neurodegenerative dementia, and identification at the prodromal stage$-$i.e., mild cognitive impairment due to Lewy bodies (MCI-LB)$-$is important for providing appropriate care. However, MCI-LB is often underrecognized because of its diversity in clinical manifestations and similarities with other conditions such as mild cognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we propose a machine learning-based automatic pipeline that helps identify MCI-LB by exploiting balance measures acquired with an insole sensor during a 30-s standing task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46 cognitively normal) showed that the resultant models could discriminate MCI-LB from the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8% better than the accuracy of a reference model based on demographic and clinical neuropsychological measures. Our findings may open up a new approach for timely identification of MCI-LB, enabling better care for patients.