دورية أكاديمية
Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment
| العنوان: | Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment |
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| المؤلفون: | Young Chul Youn, Jung-Min Pyun, Nayoung Ryu, Min Jae Baek, Jae-Won Jang, Young Ho Park, Suk-Won Ahn, Hae-Won Shin, Kwang-Yeol Park, Sang Yun Kim |
| المصدر: | Alzheimer’s Research & Therapy, Vol 13, Iss 1, Pp 1-7 (2021) |
| بيانات النشر: | BMC, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Neurosciences. Biological psychiatry. Neuropsychiatry LCC:Neurology. Diseases of the nervous system |
| مصطلحات موضوعية: | Clock Drawing Test, Cognitive impairment, Convolutional neural network, Machine learning, Rey–Osterrieth Complex Figure Test, TensorFlow, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1758-9193 |
| Relation: | https://doaj.org/toc/1758-9193 |
| DOI: | 10.1186/s13195-021-00821-8 |
| URL الوصول: | https://doaj.org/article/1298e6266cd14b67a558ad88ddfa247c |
| رقم الأكسشن: | edsdoj.1298e6266cd14b67a558ad88ddfa247c |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 17589193 |
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| DOI: | 10.1186/s13195-021-00821-8 |