scholarly journals Using Variational Autoencoder to Develop and Validate a Compact, Deep Representation of Digital Clock Drawing Test for Classifying Dementia

2022 ◽  
Author(s):  
Sabyasachi Bandyopadhyay ◽  
Catherine Dion ◽  
David J. Libon ◽  
Patrick Tighe ◽  
Catherine Price ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Model ◽  
Operating Characteristics ◽  
Large Dataset ◽  
Domain Experts ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
Latent Space ◽  
Variational Autoencoder

Abstract The Clock Drawing Test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a semi-supervised deep learning (DL) system using Variational Autoencoder (VAE) can extract atypical clock features from a large dataset of unannotated CDTs (n=13,580) and use them to classify dementia (n=18) from non-dementia (n=20) peers. The classification model built with VAE latent space features adequately classified dementia from non-dementia (0.78 Area Under Receiver Operating Characteristics (AUROC)). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a semi-supervised deep learning (DL) analysis of the CDT can extract important clock drawing anomalies that are predictive of dementia.

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

2021 ◽  
pp. 1-9
Author(s):  
Samad Amini ◽  
Lifu Zhang ◽  
Boran Hao ◽  
Aman Gupta ◽  
Mengting Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Cost Effective ◽  
Learning Model ◽  
Modeling Framework ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test

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.

scholarly journals Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5239
Author(s):  
Ingyu Park ◽  
Unjoo Lee
Keyword(s):  
Deep Learning ◽  
Scoring System ◽  
Learning Algorithms ◽  
Sensor Data ◽  
Smart Devices ◽  
Mobile Sensor ◽  
Convolutional Network ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test

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.

scholarly journals An AI-assisted Online Tool for Cognitive Impairment Detection Using Images from the Clock Drawing Test

2021 ◽  
Author(s):  
Samad Amini ◽  
Lifu Zhang ◽  
Boran Hao ◽  
Aman Gupta ◽  
Mengting Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Screening Tool ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Deep Learning Algorithm ◽  
Drawing Test ◽  
Deployable Mechanism

AbstractBackgroundWidespread early dementia detection could drastically increase clinical trial candidates and enable early interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia related diseases, it can be leveraged to devise a computer-aided screening tool.ObjectiveThis work aims to develop an online screening tool by leveraging Artificial Intelligence and the CDT.MethodsImages of an analog clock drawn by 3, 263 cognitively intact and 160 impaired subjects were used. First, we processed the images from the CDT by a deep learning algorithm to obtain dementia scores. Then, individuals were classified as belonging to either category by combining CDT image scores with the participant’s age.ResultsWe have evaluated the performance of the developed models by applying 5-fold cross validation on 20% of the dataset. The deep learning model generates dementia scores for the CDT images with an Area Under the ROC Curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age and the generated dementia scores, yielded an average AUC and average weighted F1 score of 92% ± 0.8% and 94.4% ± 0.7%, respectively.DiscussionCDT images were subjected to distortion consistent with an image drawn on paper and photographed by a cell phone. The model offers a cost-effective and easily deployable mechanism for detecting cognitive impairment online, without the need to visit a clinic.

Clock drawing test in mild cognitive impairment: Correlation with cerebral perfusion in single-photon emission computed tomography.

2019 ◽  
Vol 33 (5) ◽  
pp. 617-632 ◽  
Author(s):  
Diana Duro ◽  
Pedro Cerveira ◽  
Beatriz Santiago ◽  
Maria João Cunha ◽  
João Manuel Pedroso de Lima ◽  
...  
Keyword(s):  
Cerebral Perfusion ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Single Photon Emission ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
Single Photon Emission Computed ◽  
Photon Emission Computed Tomography

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 ◽  
...  
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.

scholarly journals Digital Clock Drawing: Differentiating “Thinking” versus “Doing” in Younger and Older Adults with Depression

2014 ◽  
Vol 20 (9) ◽  
pp. 920-928 ◽  
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)

The Four-Point Scoring System for the Clock Drawing Test Does Not Differentiate between Alzheimer's Disease and Vascular Dementia

2010 ◽  
Vol 106 (3) ◽  
pp. 941-948 ◽  
Author(s):  
April R. Wiechmann ◽  
James R. Hall ◽  
Sid O'bryant
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Sensitivity And Specificity ◽  
Scoring System ◽  
Cutoff Score ◽  
Characteristic Analysis ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test

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.

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

2015 ◽  
Vol 102 (3) ◽  
pp. 393-441 ◽  
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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