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.
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Understanding How Sensory Changes Experienced by Individuals with a Range of Age-Related Cognitive Changes Can Affect Technology Use
Clinical researchers have identified sensory changes people with age-related cognitive changes, such as dementia and mild cognitive impairment, experience that are different from typical age-related sensory changes. Technology designers and researchers do not yet have an understanding of how these unique sensory changes affect technology use. This work begins to bridge the gap between the clinical knowledge of sensory changes and technology research and design through interviews with people with mild to moderate dementia, mild cognitive impairment, subjective cognitive decline, and healthcare professionals. This extended version of our ASSETS conference paper includes people with a range of age-related cognitive changes describing changes in vision, hearing, speech, dexterity, proprioception, and smell. We discuss each of these sensory changes and ways to leverage optimal modes of sensory interaction for accessible technology use with existing and emerging technologies. Finally, we discuss how accessible sensory stimulation may change across the spectrum of age-related cognitive changes.
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- Award ID(s):
- 2045679
- PAR ID:
- 10352710
- Date Published:
- Journal Name:
- ACM Transactions on Accessible Computing
- Volume:
- 15
- Issue:
- 2
- ISSN:
- 1936-7228
- Page Range / eLocation ID:
- 1 to 33
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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