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An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.
- Source :
- Journal of Alzheimer's Disease; 2021, Vol. 83 Issue 2, p581-589, 9p
- Publication Year :
- 2021
-
Abstract
- <bold>Background: </bold>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.<bold>Objective: </bold>To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusion: </bold>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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
RECEIVER operating characteristic curves
COGNITION disorders
DEMENTIA
MILD cognitive impairment
DIAGNOSIS of dementia
RESEARCH
RESEARCH methodology
MEDICAL screening
ARTIFICIAL intelligence
MEDICAL cooperation
EVALUATION research
NEUROPSYCHOLOGICAL tests
COMPARATIVE studies
RESEARCH funding
LONGITUDINAL method
Subjects
Details
- Language :
- English
- ISSN :
- 13872877
- Volume :
- 83
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Alzheimer's Disease
- Publication Type :
- Academic Journal
- Accession number :
- 152692580
- Full Text :
- https://doi.org/10.3233/JAD-210299