Full text

Turn on search term navigation

© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background:Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method.

Objective:This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices.

Methods:This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant’s hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment–screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).

Results:We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.

Conclusions:The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants’ cognitive impairment compared to conventional paper-based feature assessment.

Details

Title
Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study
Author
Zhang, Wei  VIAFID ORCID Logo  ; Zheng, Xiaoran  VIAFID ORCID Logo  ; Tang, Zeshen  VIAFID ORCID Logo  ; Wang, Haoran  VIAFID ORCID Logo  ; Li, Renren  VIAFID ORCID Logo  ; Xie, Zengmai  VIAFID ORCID Logo  ; Yan, Jiaxin  VIAFID ORCID Logo  ; Zhang, Xiaochen  VIAFID ORCID Logo  ; Yu, Qing  VIAFID ORCID Logo  ; Wang, Fei  VIAFID ORCID Logo  ; Li, Yunxia  VIAFID ORCID Logo 
First page
e42637
Section
Artificial Intelligence
Publication year
2023
Publication date
2023
Publisher
Gunther Eysenbach MD MPH, Associate Professor
e-ISSN
1438-8871
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2917628526
Copyright
© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.