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Developing an Artificial Intelligence-Based Computerized Digit Vigilance Test for Community-Dwelling Older Adults
Gong-Hong Lin;
Dorothy Bai;
Yi-Jing Huang;
Shih-Chieh Lee;
Mai Thi Thuy Vu;
Tsu-Hsien Chiu
ABSTRACT
Background:
The Computerized Digit Vigilance Test (CDVT) is a well-established measure of sustained attention. However, the CDVT fails to capture other crucial attentional features such as the eye blink rate, yawns, head movements, and eye movements.
Objective:
This study aimed to develop an artificial intelligence (AI)-based CDVT (AI-CDVT) in older adults.
Methods:
Participants were assessed by the CDVT with video recordings capturing their head and face. The AI-CDVT was developed through (1) retrieving attentional features, (2) establishing an AI-based scoring model, and (3) assessing the validity and test-retest reliability.
Results:
Pearson’s r values of the AI-CDVT with the CDVT were 0.97 (N=153), -0.31–-0.42 with the Montreal Cognitive Assessment and Stroop Color Word Test, and 0.46–0.61 with Color Trails Test. The intraclass correlation coefficient was 0.78.
Conclusions:
Leveraging AI to extract attentional features from video recordings and integrating them to generate a comprehensive attention score is workable to assess attention. Clinical Trial: NA
Citation
Please cite as:
Lin GH, Bai D, Huang YJ, Lee SC, Vu MTT, Chiu TH
Artificial Intelligence–Based Computerized Digit Vigilance Test in Community-Dwelling Older Adults: Development and Validation Study