Drawing Test
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Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5239
Ingyu Park ◽  
Unjoo Lee

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0–3 points), numbers (0–4 points), hands (0–5 points), and the center (0–1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field.

2021 ◽  
pp. 1-9
Samad Amini ◽  
Lifu Zhang ◽  
Boran Hao ◽  
Aman Gupta ◽  
Mengting Song ◽  

Background: Widespread 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. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images 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. Results: When 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. Conclusion: Our 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.

2021 ◽  
Vol 13 (13) ◽  
pp. 7383
Juliet Jue ◽  
Jung-Hee Ha

In this study, we investigated how effectively a Draw-a-Story drawing test can predict the perceived stress, military life adjustment, and resilience of soldiers. A total of 276 conscripted male soldiers participated in the study. The research tools included the Perceived Stress Scale, the Military Life Adjustment Scale, the Connor–Davidson Resilience Scale (Korean version), and the Draw-a-Story drawing test. The results of the correlation and regression analyses were as follows. First, perceived stress, military life adjustment, resilience, and DAS emotional content were all mutually correlated. The DAS self-image was positively correlated only with resilience. Second, emotional content predicted soldiers’ perceived stress, military life adjustment, and resilience at significant levels. Meanwhile, our regression analysis showed that self-image did not have significant predictive power. In this paper, we discuss the implications for predictive power of our findings regarding the two measures of DAS; we also propose that DAS could serve as a tool to predict the mental states of soldiers.

O.F. Lysenko ◽  
M.V. Safonova ◽  

Statement of the problem. The article presents an analysis and discussion of the results of diagnostic assessment among pregnant women, allowing us to draw conclusions on indicators of psychological preparedness for motherhood. The purpose the article is to outline the necessary areas of work with women on the way to the formation of the maternal sphere in ontogenesis. Materials and Methods. The research methodology consists of the concept of the formation of the “maternal need-motivation sphere” by G.G. Filippova, the theory of E.V. Matveeva about psychological readiness for motherhood, as well as the analysis and synthesis of studies on motherhood, psychological readiness for motherhood, questions of perinatal psychology, theory of psychological readiness for activity. The study involved 156 married pregnant women aged 25 to 35 with higher or secondary specialized education, with a pregnancy period of no less than 24 weeks. The following psychodiagnostic methods were applied: the projective drawing test “I and my child” by G.G. Filippova, the modified Dembo-Rubinstein scale by G.G. Filippova, test of the relationship of pregnant I.V. Dobryakov’s test on relationships for pregnant women, and the authors’ questionnaire for pregnant women. Research results. The diagnostic results indicate that 79,3 % of women examined are conditionally ready for motherhood, 15 % are not ready, and only 5,7 % of respondents can be considered psychologically ready for motherhood. The last group of women is characterized by unconditional acceptance of a child, themselves as a mother, and the situation of motherhood in general. The correlation and factor analysis of the obtained data made it possible to distinguish four groups of the most informative indicators of psychological readiness for motherhood in pregnant women: the peculiarities of the current situation of the woman, the mother-and-child system, family relations and the social and domestic situation. There are also widely presented factors that make it difficult to form a psychological readiness for motherhood: unfavorable experience of relationships with their own parents, difficulties in establishing emotional contact, lack of knowledge and experience in caring for children. There is a need to work with these aspects before pregnancy. Conclusions are drawn on the importance of comprehensive psychological and pedagogical support of a woman on the way to the formation of the maternal sphere of her personality in relation to genesis, which contributes to the development of psychological preparedness for motherhood.

2021 ◽  
Vol 82 (1) ◽  
pp. 47-57 ◽  
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.

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