A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening

Article type: Research Article

Authors: Chen, Lihua^a | Zhang, Meiwei^b | Yu, Weihua^c | Yu, Juan^b | Cui, Qiushi^b | Chen, Chenxi^a | Liu, Junjin^a | Huang, Lihong^c | Liu, Jiarui^a | Yu, Wuhan^a | Li, Wenjie^a | Zhang, Wenbo^a | Yan, Mengyu^c | Wu, Jiani^a | Wang, Xiaoqin^a | Song, Jiaqi^c | Zhong, Fuxing^a | Liu, Xintong^a | Wang, Xianglin^d | Li, Chengxing^d | Tan, Yuantao^d | Sun, Jiangshan^d | Li, Wenyuan^{b; *} | Lü, Yang^{a; *}

Affiliations: [a] Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China | [b] College of Electrical Engineering, Chongqing University, Chongqing, China | [c] Department of Human Anatomy, Institute of Neuroscience, Chongqing Medical University, Chongqing, China | [d] College of Computer Science, Chongqing University, Chongqing, China

Correspondence: [*] Correspondence to: Prof. Yang Lü, No.1 Youyi Road, Yuzhong District, Chongqing 400016, China. E-mail: yanglyu@hospital.cqmu.edu.cn and Prof. Wenyuan Li, No. 174 Shazhengjie, Shapingba, Chongqing 400030, China. E-mail: wenyuan.li@ieee.org.

Abstract: Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model’s rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items “Reading and obey”, “Three-stage command”, and “Writing complete sentence” were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and −3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.

Keywords: Alzheimer’s disease, automated assessment, cognitive function, computer-related technologies, cognitive screening, Mini-Mental State Examination

DOI: 10.3233/JAD-230518

Journal: Journal of Alzheimer's Disease, vol. 97, no. 4, pp. 1661-1672, 2024