ArticleAlzheimer's research & therapy2021
Use of the Clock Drawing Test and the Rey-Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment.
Article in Alzheimer's research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 48 citations in OpenAlex.
- Circuit-based neuromodulation enhances delayed recall in amnestic mild cognitive impairment.Journal of neurology, neurosurgery, and psychiatry · 2024Trial
- A novel vision transformer model produces clock drawing test scores as accurate as expert human coders.Scientific reports · 2026Article
- Resting-state EEG biomarkers of general and domain-specific cognitive impairment and plasma P-tau217 in patients with Alzheimer's disease and mild cognitive impairment.Frontiers in neurology · 2026Article
- Unilateral prosopometamorphopsia caused by infarction of the splenium of the corpus callosum: 4 case report and review of the literature.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Psychometric Properties of a Neuropsychological Assessment Scale for Diagnosing Alzheimer's Disease in the Illiterate and Low-Educated Subjects in Iran.Iranian journal of psychiatry · 2025Article
- PENSIEVE-AI a brief cognitive test to detect cognitive impairment across diverse literacy.Nature communications · 2025Article
- Automated versus human scoring of the Rey-Osterrieth Complex Figure Test: a rapid review.Frontiers in psychiatry · 2025Review
- Previous parity differentially influences cognition in later life depending on dementia status.NPJ dementia · 2025Article
- A Telemedicine Analytic Framework for Fully and Semi-Automatic Alzheimer's Disease Screening Using Clock Drawing Test.IEEE journal of biomedical and health informatics · 2024Article
- The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review.Journal of clinical medicine · 2024Review
- Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.Scientific reports · 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
- 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
- An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.Alzheimer's research & therapy · 2022Article
- Deep convolutional neural networks for automated scoring of pentagon copying test results.Scientific reports · 2022Article
- Automated Evaluation of Conventional Clock-Drawing Test Using Deep Neural Network: Potential as a Mass Screening Tool to Detect Individuals With Cognitive Decline.Frontiers in neurology · 2022Article
- Exploratory Research on Key Technology of Human-Computer Interactive 2.5-Minute Fast Digital Early Warning for Mild Cognitive Impairment.Computational intelligence and neuroscience · 2022Article
- Modeling Users' Cognitive Performance Using Digital Pen Features.Frontiers in artificial intelligence · 2022Article
- Visuospatial memory impairment as a potential neurocognitive marker to predict tau pathology in Alzheimer's continuum.Alzheimer's research & therapy · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Clock Drawing Test (CDT) and Rey-Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool.
methodsThe CDT and RCFT-copy data were obtained from patients aged 60-80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform ( www.colab. RESEARCH: google.com ) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI).
resultsThe accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them.
conclusionsThe two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.
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