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Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy With Convolutional Neural Networks to Predict Cognitive Impairment
SangYun Kim
Seoul National University College of Medicine & Neurocognitive Behavior Center, Seoul National University Bundang Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9101-5704
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using the convolutional neural network (CNN) algorithm as a screening tool.
Methods: The CDT and RCFT-copy data were obtained from patients aged 60 to 80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. CNN algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform (www.colab.research.google.com) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset: normal cognition (NC) vs. mild impairment of cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI +SI).
Results: The accuracy of the CDT was better for differentiating MI (CDT: 78.04±2.75, RCFT-copy: not being trained) and SI from NC (CDT: 91.45±0.83, RCFT-copy: 90.27±1.52); however, the RCFT-copy was better at predicting CI (CDT: 77.37±1.77, RCFT: 83.52±1.41). The accuracy for 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found.
Conclusions: The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.
Keywords
Convolutional neural network, Clock Drawing Test, Rey–Osterrieth Complex Figure Test, Cognitive impairment, Machine learning, TensorFlow
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Subject Areas
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This preprint is under consideration at Alzheimer's Research and Therapy. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy With Convolutional Neural Networks to Predict Cognitive Impairment
SangYun Kim
Seoul National University College of Medicine & Neurocognitive Behavior Center, Seoul National University Bundang Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9101-5704
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Dec, 2020
No community comments so far
Review #2 received
Received 02 Jan, 2021
Reviewer #2 agreed
On 31 Dec, 2020
Reviewer #1 agreed
On 30 Dec, 2020
Reviewers invited
Invitations sent on 29 Nov, 2020
First submitted
On 28 Nov, 2020
Editor assigned
On 28 Nov, 2020
Submission checks complete
On 28 Nov, 2020
Editor invited
On 28 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cognitive Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using the convolutional neural network (CNN) algorithm as a screening tool.
Methods: The CDT and RCFT-copy data were obtained from patients aged 60 to 80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. CNN algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform (www.colab.research.google.com) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset: normal cognition (NC) vs. mild impairment of cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI +SI).
Results: The accuracy of the CDT was better for differentiating MI (CDT: 78.04±2.75, RCFT-copy: not being trained) and SI from NC (CDT: 91.45±0.83, RCFT-copy: 90.27±1.52); however, the RCFT-copy was better at predicting CI (CDT: 77.37±1.77, RCFT: 83.52±1.41). The accuracy for 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found.
Conclusions: The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.
Figure 1