An Exploratory Study Integrating Deep Learning in Digital Clock Drawing Test on Consumer Platforms for Enhanced Detection of Mild Cognitive Impairment

被引:0
作者
Kuok, Bryan Zi Wei [1 ]
Koh, Malcolm H. S. [1 ]
Lim, Kenneth Y. T. [1 ]
机构
[1] Nanyang Technol Univ, Natl Inst Educ, 1 Nanyang Walk, Singapore 1, Singapore
来源
HCI INTERNATIONAL 2024 POSTERS, PT II, HCII 2024 | 2024年 / 2115卷
关键词
Early dementia diagnosis; data acquisition and data quality; facilitation of early therapeutic intervention;
D O I
10.1007/978-3-031-61947-2_20
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Dementia is set to become a major global health challenge. Studies show that there is a significant surge in cases of adults above 40 living with dementia. This alarming increase is due to various factors and early detection of dementia can allow for proper treatment to be administered. There exist various screening methods, but all come with shortcomings, particularly, subjectivity in hand-scored tests. Digitalisation of the toolsmitigates this concern but can bring about newer concerns such as feasibility. The clock drawing test is a simple pen and paper test, to differentiate normal individuals from those with cognitive impairment, such as dementia. Our project aims to enhance the use of the clock drawing test, via digitalising it and equipping it with machine learning capabilities. We aim to offer an increased potential for early detection of cognitive impairment, to overall, improve the state of dementia detection already in practice.
引用
收藏
页码:175 / 181
页数:7
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