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 ◽  
...  

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 ◽  
Author(s):  
Samad Amini ◽  
Lifu Zhang ◽  
Boran Hao ◽  
Aman Gupta ◽  
Mengting Song ◽  
...  

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.


2016 ◽  
Vol 10 (3) ◽  
pp. 227-231 ◽  
Author(s):  
Bárbara Costa Beber ◽  
Renata Kochhann ◽  
Bruna Matias ◽  
Márcia Lorena Fagundes Chaves

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.


2009 ◽  
Vol 22 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Lena Ehreke ◽  
Melanie Luppa ◽  
Hans-Helmut König ◽  
Steffi G. Riedel-Heller

ABSTRACTBackground:The clock drawing test (CDT) is a common and widely used cognitive screening instrument for the diagnosis of dementia. However, it has remained unclear whether it is a suitable method to identify mild cognitive impairment (MCI). The aim of this paper is to review systematically the studies concerning the utility of the CDT in diagnosing MCI.Method:A systematic literature search was conducted. All studies dealing with utility of CDT in diagnosing MCI regardless of the applied CDT scoring system and MCI concept were selected.Results:Nine relevant studies were identified. The majority of the studies compared average CDT scores of cognitively healthy and mildly impaired subjects, and four of them identified significant mean differences. If reported, sensitivity and specificity have been mostly unsatisfactory.Conclusion:CDT should not be used for MCI-screening.


2010 ◽  
Vol 22 (3) ◽  
pp. 889-896 ◽  
Author(s):  
Jesús Cacho ◽  
Julián Benito-León ◽  
Ricardo García-García ◽  
Bernardino Fernández-Calvo ◽  
José Luis Vicente-Villardón ◽  
...  

2006 ◽  
Vol 14 (7S_Part_15) ◽  
pp. P841-P842
Author(s):  
Natasha A. Talwar ◽  
Nathan W. Churchill ◽  
Megan A. Hird ◽  
Tahira Tasneem ◽  
Iryna Pshonyak ◽  
...  

Author(s):  
K. Kudukhova ◽  
L. Ivanova ◽  
V. Khaikin ◽  
V. Mkrtchyan

The purpose of this study is assessing informative capability of the most frequently used scales and neuropsychological tests evaluating cognitive function for mild cognitive impairment (MCI) and vascular dementia (VD). A total of 104 patients with cerebrovascular disorder including 39 male and 65 female were divided into two subgroups depending on severity of the cognitive impairment. The first group consisted of 51 patients with MCI and the second one consisted of 53 patients with VD confirmed by MMSE and MoCA-test. The obtained correlation analysis data testifies to difficulties in the interpretation of these routinely used scales not only because of the differences between investigated parameters of the patients with MCI and VD, but also because of the main goal of their creation. Most of the scales were created for patients with dementia and now their sensitivity for MCI is doubtful. The MoCA test, Clock Drawing Test (CDT) and ADAS-cog subscale are more sensitive and has greatest informative capability for patients with MCI and dementia, while MMSE is more informative mostly for patients with dementia. Neuropsychological tests DAD and NPI remain informative in varying degrees of cognitive impairment. In the diagnosis and differential diagnosis of the degree of cognitive impairment of vascular genesis, one should use not only a complex of scales and neuropsychological tests, but also methods that comprehensively reflect the vascular genesis of the process of formation of cognitive impairment.


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