ArticleJournal of Alzheimer's disease : JAD2021
An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the 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 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- The myth of digital biomarkers in Alzheimer's disease: how to make them a reality.Current opinion in psychiatry · 2026Review
- 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
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- MCI detection from handwritten drawing test using residual vision transformer.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
- PENSIEVE-AI a brief cognitive test to detect cognitive impairment across diverse literacy.Nature communications · 2025Article
- Caregiver perspectives enable accurate diagnosis of neurodegenerative disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.BMC neurology · 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
- Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review.Journal of clinical medicine · 2022Review
- Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.BMC medicine · 2022Article
- Modeling Users' Cognitive Performance Using Digital Pen Features.Frontiers in artificial intelligence · 2022Article
- Redefining and Validating Digital Biomarkers as Fluid, Dynamic Multi-Dimensional Digital Signal Patterns.Frontiers in digital health · 2021Article
- 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
10 authors.
Funding
Abstract
backgroundWidespread 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.
objectiveTo evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia.
methodsImages 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.
resultsWhen 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.
conclusionOur 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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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.