ArticleJournal of Alzheimer's disease : JAD2021
Classifying Non-Dementia and Alzheimer's Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Validation of an artificial intelligence-based drawing platform against the Clock Drawing Test for cognitive screening: a comparative study between Alzheimer's disease and healthy controls.Family practice · 2026Article
- Predicting cognitive impairment using novel functional features of spatial proximity and circularity in the digital clock drawing test.medRxiv : the preprint server for health sciences · 2026Article
- Streamlining Eligibility Assessment for Alzheimer's Disease-Modifying Therapies: Prediction of MMSE Scores Using the Digital Clock and Recall.medRxiv : the preprint server for health sciences · 2026Article
- Higher Amyloid and Tau Burden Is Associated With Faster Decline on a Digital Cognitive Test.Annals of clinical and translational neurology · 2026Article
- Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.Journal of the International Neuropsychological Society : JINS · 2026Article
- MCI detection from handwritten drawing test using residual vision transformer.Scientific reports · 2026Article
- Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall.Frontiers in digital health · 2026Article
- Concurrent detection of cognitive impairment and amyloid positivity with a multimodal machine learning-enabled digital cognitive assessment.Alzheimer's research & therapy · 2025Article
- Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes.Research square · 2025Article
- Article
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Adherence timescale impacts completion rates of high-frequency mobile cognitive assessments among older adults.Exploration of medicine · 2025Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Dysexecutive difficulty and subtle everyday functional disabilities: the digital Trail Making Test.Frontiers in neurology · 2024Article
- Digital assessment of cognition in neurodegenerative disease: a data driven approach leveraging artificial intelligence.Frontiers in psychology · 2024Article
- Investigating the associations between upper limb motor function and cognitive impairment: a scoping review.GeroScience · 2023Article
- Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.Scientific reports · 2023Article
- Adherence type impacts completion rates of frequent mobile cognitive assessments among older adults with and without cognitive impairment.Research square · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundAdvantages of digital clock drawing metrics for dementia subtype classification needs examination.
objectiveTo assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer's disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer's disease (AD) versus vascular dementia (VaD).
methodsHealthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer's disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models.
resultsWhen HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features.
conclusionThe dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.
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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.