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An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Authors :
Samad Amini
Ioannis Ch. Paschalidis
Vijaya B. Kolachalama
Lifu Zhang
Honghuang Lin
Aman Gupta
Rhoda Au
Mengting Song
Cody Karjadi
Boran Hao
Source :
Journal of Alzheimer's Disease. 83:581-589
Publication Year :
2021
Publisher :
IOS Press, 2021.

Abstract

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Details

ISSN :
18758908 and 13872877
Volume :
83
Database :
OpenAIRE
Journal :
Journal of Alzheimer's Disease
Accession number :
edsair.doi...........d28afde6e3b2d8765a633e7bec3eff90
Full Text :
https://doi.org/10.3233/jad-210299