ArticleFrontiers in neurology2022
Automated Evaluation of Conventional Clock-Drawing Test Using Deep Neural Network: Potential as a Mass Screening Tool to Detect Individuals With Cognitive Decline.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- MCI detection from handwritten drawing test using residual vision transformer.Scientific reports · 2026Article
- A novel vision transformer model produces clock drawing test scores as accurate as expert human coders.Scientific reports · 2026Article
- Developing an accessible dementia assessment tool: Leveraging a residual network, the trail making test, and demographic data.Journal of Alzheimer's disease : JAD · 2025Article
- Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.Sensors (Basel, Switzerland) · 2025Article
- The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review.Journal of clinical medicine · 2024Review
- Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias.Scientific reports · 2024Article
- Clock-drawing Test and Cube-copying Test to Quickly Screen Dementia: In Combination with the Mini-mental State Examination Scores.Internal medicine (Tokyo, Japan) · 2024Article
- Non-equivalence of sub-tasks of the Rey-Osterrieth Complex Figure Test with convolutional neural networks to discriminate mild cognitive impairment.BMC psychiatry · 2024Article
- Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.Scientific reports · 2023Article
- FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups.Research square · 2023Article
- Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline.Alzheimer's research & therapy · 2023Article
- Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons.NPJ digital medicine · 2023Article
- Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.Scientific reports · 2023Article
- The current state of artificial intelligence-augmented digitized neurocognitive screening test.Frontiers in human neuroscience · 2023Review
- A vision transformer approach for fully automated and scalable dementia screening using clock drawing test images.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The Clock-Drawing Test (CDT) is a simple cognitive tool to examine multiple domains of cognition including executive function. We aimed to build a CDT-based deep neural network (DNN) model using data from a large cohort of older adults, to automatically detect cognitive decline, and explore its potential as a mass screening tool. Methods: Over 40,000 CDT images were obtained from the National Health and Aging Trends Study (NHATS) database, which collects the annual surveys of nationally representative community-dwelling older adults in the United States. A convolutional neural network was utilized in deep learning architecture to predict the cognitive status of participants based on drawn clock images. Results: The trained DNN model achieved balanced accuracy of 90.1 ± 0.6% in identifying those with a decline in executive function compared to those without [positive likelihood ratio (PLH) = 16.3 ± 6.8, negative likelihood ratio (NLH) = 0.14 ± 0.03], and 77.2 ± 2.7 % balanced accuracy for identifying those with probable dementia from those without (PLH = 5.1 ± 0.5, NLH = 0.37 ± 0.07). Conclusions: This study demonstrated the feasibility of implementing conventional CDT to be automatically evaluated by DNN with a fair performance in a larger scale than ever, suggesting its potential as a mass screening test for ruling-in or ruling-out those with executive dysfunction or with probable dementia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.