DCDT: A Digital Clock Drawing Test System for Cognitive Impairment Screening

AbstractObjective:To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).Methods:dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.Results:Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.Conclusion:Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

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Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test*

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9176469 ◽

2020 ◽

Author(s):

Anis Davoudi ◽

Catherine Dion ◽

Shawna Amini ◽

David J. Libon ◽

Patrick J. Tighe ◽

...

Keyword(s):

Cognitive Impairment ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

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Extended Application of Digital Clock Drawing Test in the Evaluation of Alzheimer’s Disease Based on Artificial intelligence and the Neural Basis

Current Alzheimer Research ◽

10.2174/1567205018666211210150808 ◽

2021 ◽

Vol 18 ◽

Author(s):

Xiaoran Zheng ◽

Xing Wang ◽

Wei Zhang ◽

Renren Li ◽

Meng Liu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Cognitive Domain ◽

Neural Basis ◽

Cognitive Domains ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

Introduction: This study aimed to build the supervised learning model to predict the state of cognitive impairment, Alzheimer’s Disease (AD) and cognitive domains including memory, language, action, and visuospatial based on Digital Clock Drawing Test (dCDT) precisely. Methods: 207 normal controls, 242 Mild Cognitive Impairment (MCI) patients, 87 dementia patients, including 53 AD patients, were selected from Shanghai Tongji Hospital. The electromagnetic tablets were used to collect the trajectory points of dCDT. By combining dynamic process and static results, different types of features were extracted, and the prediction models were built based on the feature selection approaches and machine learning methods. Results: The optimal AUC of cognitive impairment’s screening, AD’s screening and differentiation are 0.782, 0.919 and 0.818, respectively. In addition, the cognitive state of the domains with the best prediction result based on the features of dCDT is action with the optimal AUC 0.794, while the other three cognitive domains got the prediction results between 0.744-0.755. Discussion: By extracting dCDT features, cognitive impairment and AD patients can be identified early. Through dCDT feature extraction, a prediction model of single cognitive domain damage can be established.

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Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

Journal of Alzheimer s Disease ◽

10.3233/jad-201129 ◽

2021 ◽

Vol 82 (1) ◽

pp. 47-57 ◽

Cited By ~ 1

Author(s):

Anis Davoudi ◽

Catherine Dion ◽

Shawna Amini ◽

Patrick J. Tighe ◽

Catherine C. Price ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Vascular Dementia ◽

Area Under The Curve ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test ◽

Dementia Patients ◽

Optimal Area

Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To 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). Methods: Healthy, 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. Results: When 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. Conclusion: The 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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Digital Clock Drawing: Differentiating “Thinking” versus “Doing” in Younger and Older Adults with Depression

Journal of the International Neuropsychological Society ◽

10.1017/s1355617714000757 ◽

2014 ◽

Vol 20 (9) ◽

pp. 920-928 ◽

Cited By ~ 15

Author(s):

Jamie Cohen ◽

Dana L. Penney ◽

Randall Davis ◽

David J. Libon ◽

Rodney A. Swenson ◽

...

Keyword(s):

Motor Performance ◽

Psychomotor Speed ◽

Psychomotor Slowing ◽

Younger Adults ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Time To Completion ◽

Drawing Test ◽

Depressed Group

AbstractPsychomotor slowing has been documented in depression. The digital Clock Drawing Test (dCDT) provides: (i) a novel technique to assess both cognitive and motor aspects of psychomotor speed within the same task and (ii) the potential to uncover subtleties of behavior not previously detected with non-digitized modes of data collection. Using digitized pen technology in 106 participants grouped by Age (younger/older) and Affect (euthymic/unmedicated depressed), we recorded cognitive and motor output by capturing how the clock is drawn rather than focusing on the final product. We divided time to completion (TTC) for Command and Copy conditions of the dCDT into metrics of percent of drawing (%Ink) versus non-drawing (%Think) time. We also obtained composite Z-scores of cognition, including attention/information processing (AIP), to explore associations of %Ink and %Think times to cognitive and motor performance. Despite equivalent TTC, %Ink and %Think Command times (Copy n.s.) were significant (AgeXAffect interaction: p=.03)—younger depressed spent a smaller proportion of time drawing relative to thinking compared to the older depressed group. Command %Think time negatively correlated with AIP in the older depressed group (r=−.46; p=.02). Copy %Think time negatively correlated with AIP in the younger depressed (r=−.47; p=.03) and older euthymic groups (r=−.51; p=.01). The dCDT differentiated aspects of psychomotor slowing in depression regardless of age, while dCDT/cognitive associates for younger adults with depression mimicked patterns of older euthymics. (JINS, 2014, 20, 1–9)

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The Clock-Drawing Test in Elderly Italian Community Dwellers: Associations with Sociodemographic Status and Risk Factors for Vascular Cognitive Impairment

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000072815 ◽

2003 ◽

Vol 16 (4) ◽

pp. 287-295 ◽

Cited By ~ 15

Author(s):

G. Ravaglia ◽

P. Forti ◽

F. Maioli ◽

G. Arnone ◽

G. Pantieri ◽

...

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Vascular Cognitive Impairment ◽

Clock Drawing Test ◽

Clock Drawing ◽

Drawing Test ◽

Italian Community ◽

Community Dwellers

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Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test

Machine Learning ◽

10.1007/s10994-015-5529-5 ◽

2015 ◽

Vol 102 (3) ◽

pp. 393-441 ◽

Cited By ~ 42

Author(s):

William Souillard-Mandar ◽

Randall Davis ◽

Cynthia Rudin ◽

Rhoda Au ◽

David J. Libon ◽

...

Keyword(s):

Classification Models ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

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The Clock Drawing Test: Performance differences between the free-drawn and incomplete-copy versions in patients with MCI and dementia

Dementia & Neuropsychologia ◽

10.1590/s1980-5764-2016dn1003009 ◽

2016 ◽

Vol 10 (3) ◽

pp. 227-231 ◽

Cited By ~ 2

Author(s):

Bárbara Costa Beber ◽

Renata Kochhann ◽

Bruna Matias ◽

Márcia Lorena Fagundes Chaves

Keyword(s):

Cognitive Impairment ◽

Test Performance ◽

Diagnostic Value ◽

Control Group ◽

Cognitive Screening ◽

Scoring Method ◽

Clock Drawing Test ◽

Clock Drawing ◽

Healthy Elderly ◽

Drawing Test

ABSTRACT Background: The Clock Drawing Test (CDT) is a brief cognitive screening tool for dementia. Several different presentation formats and scoring methods for the CDT are available in the literature. Objective: In this study we aimed to compare performance on the free-drawn and "incomplete-copy" versions of the CDT using the same short scoring method in Mild Cognitive Impairment (MCI) and dementia patients, and healthy elderly participants. Methods: 90 participants (controlled for age, sex and education) subdivided into control group (n=20), MCI group (n=30) and dementia group (n=40) (Alzheimer's disease - AD=20; Vascular Dementia - VD=20) were recruited for this study. The participants performed the two CDT versions at different times and a blinded neuropsychologist scored the CDTs using the same scoring system. Results: The scores on the free-drawn version were significantly lower than the incomplete-copy version for all groups. The dementia group had significantly lower scores on the incomplete-copy version of the CDT than the control group. MCI patients did not differ significantly from the dementia or control groups. Performance on the free-drawn copy differed significantly among all groups. Conclusion: The free-drawn CDT version is more cognitively demanding and sensitive for detecting mild/early cognitive impairment. Further evaluation of the diagnostic value (accuracy) of the free-drawn CDT in Brazilian MCI patients is needed.

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