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

2021 ◽  
Vol 82 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Anis Davoudi ◽  
Catherine Dion ◽  
Shawna Amini ◽  
Patrick J. Tighe ◽  
Catherine C. Price ◽  
...  

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.

2010 ◽  
Vol 106 (3) ◽  
pp. 941-948 ◽  
Author(s):  
April R. Wiechmann ◽  
James R. Hall ◽  
Sid O'bryant

The purpose of this study was to explore the sensitivity and specificity of the Clock Drawing Test by using a widely employed four-point scoring system to discriminate between patients with Alzheimer's disease or vascular dementia. Receiver operating characteristic analysis indicated that the Clock Drawing Test was able to distinguish between normal elders and those with a dementia diagnosis. The cutoff score for differentiating patients with Alzheimer's disease from normal participants was = 3. The cutoff score for differentiating those with vascular disease from normal participants was = 3. Overall, the four-point scoring system demonstrated good sensitivity and specificity for identifying cognitive dysfunction associated with dementia; however, the current findings do not support the utility of the four-point scoring system in discriminating Alzheimer's disease and vascular dementia.


2015 ◽  
Vol 11 (7S_Part_15) ◽  
pp. P710-P710
Author(s):  
Luis Jacobo Resendiz-Najera ◽  
Elena Itzel Portillo-Yañez ◽  
Gilberto Isaac Acosta-Castillo ◽  
Ana Luisa Sosa-Ortiz

2021 ◽  
Vol 18 ◽  
Author(s):  
Xiaoran Zheng ◽  
Xing Wang ◽  
Wei Zhang ◽  
Renren Li ◽  
Meng Liu ◽  
...  

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.


2006 ◽  
Vol 14 (7S_Part_7) ◽  
pp. P416-P417
Author(s):  
Jeffrey L. Dage ◽  
Chakib Battioui ◽  
Jian WJ. Wang ◽  
Arnaud Charil ◽  
Sergey Shcherbinin ◽  
...  

2015 ◽  
Vol 16 (4) ◽  
pp. 233-239 ◽  
Author(s):  
Yasushi Moriyama ◽  
Aihide Yoshino ◽  
Kaori Yamanaka ◽  
Motoichiro Kato ◽  
Taro Muramatsu ◽  
...  

2003 ◽  
Vol 59 (2-3) ◽  
pp. 173-179 ◽  
Author(s):  
Vasilis P Bozikas ◽  
Mary H Kosmidis ◽  
Anastasios Kourtis ◽  
Katerina Gamvrula ◽  
Petros Melissidis ◽  
...  

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