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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
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Youn, Young Chul | - |
dc.contributor.author | Pyun, Jung-Min | - |
dc.contributor.author | Ryu, Nayoung | - |
dc.contributor.author | Baek, Min Jung | - |
dc.contributor.author | Jang, Jae-Won | - |
dc.contributor.author | Park, Young Ho | - |
dc.contributor.author | Ahn, Suk-Won | - |
dc.contributor.author | Shin, Hae-Won | - |
dc.contributor.author | Park, Kwang-Yeol | - |
dc.contributor.author | Kim, Sang Yun | - |
dc.date.accessioned | 2021-07-13T00:20:47Z | - |
dc.date.available | 2021-07-13T09:22:26Z | - |
dc.date.issued | 2021-04-20 | - |
dc.identifier.citation | Alzheimer's Research & Therapy. 2021 Apr 20;13(1):85 | ko_KR |
dc.identifier.issn | 1758-9193 | - |
dc.identifier.uri | https://hdl.handle.net/10371/174683 | - |
dc.description.abstract | 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 convolutional neural network algorithms as a screening tool. Methods The CDT and RCFT-copy data were obtained from patients aged 60–80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network 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 with the following classes: normal cognition (NC) vs. mildly impaired 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 a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them. 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 the 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. | ko_KR |
dc.description.sponsorship | The costs for manuscript publication, design of the study, data management, and writing of the manuscript were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BMC | ko_KR |
dc.subject | Clock Drawing Test, Cognitive impairment | - |
dc.subject | Convolutional neural network | - |
dc.subject | Machine learning | - |
dc.subject | Rey–Osterrieth Complex Figure Test | - |
dc.subject | TensorFlow | - |
dc.title | Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 윤영철 | - |
dc.contributor.AlternativeAuthor | 편정민 | - |
dc.contributor.AlternativeAuthor | 유나영 | - |
dc.contributor.AlternativeAuthor | 백민재 | - |
dc.contributor.AlternativeAuthor | 장재원 | - |
dc.contributor.AlternativeAuthor | 박영호 | - |
dc.contributor.AlternativeAuthor | 안석원 | - |
dc.contributor.AlternativeAuthor | 신해원 | - |
dc.contributor.AlternativeAuthor | 박광열 | - |
dc.contributor.AlternativeAuthor | 김상윤 | - |
dc.identifier.doi | 10.1186/s13195-021-00821-8 | - |
dc.citation.journaltitle | Alzheimer's Research & Therapy | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2021-04-25T03:21:43Z | - |
dc.citation.number | 1 | ko_KR |
dc.citation.startpage | 85 | ko_KR |
dc.citation.volume | 13 | ko_KR |
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