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Title: An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test
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.  more » « less
Award ID(s):
1914792 1664644 1645681
PAR ID:
10303611
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  ;  ;  
Date Published:
Journal Name:
Journal of Alzheimer's Disease
Volume:
83
Issue:
2
ISSN:
1387-2877
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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