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.

通过利用在脚垫传感器获取的平衡度量，该研究提出了一种基于机器学习的自动化流程，可以帮助识别Lewy小体相关轻度认知功能障碍（MCI-LB），并且结果模型在区分MCI-LB与其他组别方面的准确率达到78.0%，相比基于人口统计学和临床神经心理学指标的参考模型准确率提高了6.8%，这可能为早期识别MCI-LB提供了一种新方法，以便更好地为患者提供照护。

来自鞋垫传感器的平衡指标区分前驱性帕金森病性痴呆