Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment

Springer Science and Business Media LLC - Tập 13 - Trang 1-7 - 2021
Young Chul Youn1,2, Jung-Min Pyun3, Nayoung Ryu3, Min Jae Baek3, Jae-Won Jang4, Young Ho Park3, Suk-Won Ahn1, Hae-Won Shin1, Kwang-Yeol Park1,2, Sang Yun Kim2,5
1Department of Neurology, College of Medicine, Chung-Ang University, Seoul, Republic of Korea
2Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Republic of Korea
3Department of Neurology, Seoul National University College of Medicine & Seoul National University Bundang Hospital, Seoul, Republic of Korea
4Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Republic of Korea
5Department of Neurology, Seoul National University College of Medicine & Neurocognitive Behavior Center, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

Tóm tắt

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. 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). 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. 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.

Tài liệu tham khảo

Benjamens S, Dhunnoo P, Mesko B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3(1):118. https://doi.org/10.1038/s41746-020-00324-0.

Ramkumar PN, Kunze KN, Haeberle HS, Karnuta JM, Luu BC, Nwachukwu BU, et al. Clinical and research medical applications of artificial intelligence. Arthroscopy. 2020:S0749-8063(20)30687-3. https://doi.org/10.1016/j.arthro.2020.08.009.

Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: taxonomy analysis, challenges, future solutions and methodological aspects. J Infect Public Health. 2020;13(10):1381–96. https://doi.org/10.1016/j.jiph.2020.06.028.

Aschwanden D, Aichele S, Ghisletta P, Terracciano A, Kliegel M, Sutin AR, Brown J, Allemand M. Predicting cognitive impairment and dementia: a machine learning approach. J Alzheimers Dis. 2020;75(3):717–28. https://doi.org/10.3233/JAD-190967.

Chen R, Herskovits EH. Machine-learning techniques for building a diagnostic model for very mild dementia. Neuroimage. 2010;52(1):234–44. https://doi.org/10.1016/j.neuroimage.2010.03.084.

Dallora AL, Eivazzadeh S, Mendes E, Berglund J, Anderberg P. Machine learning and microsimulation techniques on the prognosis of dementia: a systematic literature review. PLoS One. 2017;12(6):e0179804. https://doi.org/10.1371/journal.pone.0179804.

Nori VS, Hane CA, Crown WH, Au R, Burke WJ, Sanghavi DM, Bleicher P. Machine learning models to predict onset of dementia: a label learning approach. Alzheimers Dement (N Y). 2019;5(1):918–25. https://doi.org/10.1016/j.trci.2019.10.006.

Ahamed F, Shahrestani S, Cheung H. Internet of things and machine learning for healthy ageing: identifying the early signs of dementia. Sensors (Basel). 2020;20(21):6031.

Enshaeifar S, Zoha A, Markides A, Skillman S, Acton ST, Elsaleh T, Hassanpour M, Ahrabian A, Kenny M, Klein S, Rostill H, Nilforooshan R, Barnaghi P. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLoS One. 2018;13(5):e0195605. https://doi.org/10.1371/journal.pone.0195605.

Ahn HJ, Chin J, Park A, Lee BH, Suh MK, Seo SW, Na DL. Seoul Neuropsychological Screening Battery-dementia version (SNSB-D): a useful tool for assessing and monitoring cognitive impairments in dementia patients. J Korean Med Sci. 2010;25(7):1071–6. https://doi.org/10.3346/jkms.2010.25.7.1071.

Rey A. L'examen psychologique dans les cas d'encéphalopathie traumatique. (Les problems.). [The psychological examination in cases of traumatic encepholopathy. Problems.]. Archives de Psychologie. 1941;28:215–85.

Cherrier MM, Mendez MF, Dave M, Perryman KM. Performance on the Rey-Osterrieth complex figure test in Alzheimer disease and vascular dementia. Neuropsychiatry Neuropsychol Behav Neurol. 1999;12(2):95–101.

Binder LM. Constructional strategies on complex figure drawings after unilateral brain damage. J Clin Neuropsychol. 1982;4(1):51–8. https://doi.org/10.1080/01688638208401116.

Pillon B. Troubles visuo-constructifs et méthodes de compensation: Résultats de 85 patients atteints de lésions cérébrales. Neuropsychologia. 1981;19(3):375–83. https://doi.org/10.1016/0028-3932(81)90067-1.

Lezak MD. Neuropsychological assessment. 3rd. ed. New York: Oxford University Press; 1995.

Cummings JL, Benson DF. Dementia of the Alzheimer type. An inventory of diagnostic clinical features. J Am Geriatr Soc. 1986;34(1):12–9. https://doi.org/10.1111/j.1532-5415.1986.tb06334.x.

Seo EH, Kim H, Choi KY, Lee KH, Choo IH. Pre-mild cognitive impairment: can visual memory predict who rapidly convert to mild cognitive impairment? Psychiatry Investig. 2018;15(9):869–75. https://doi.org/10.30773/pi.2018.07.29.1.

Fujii DE, Lloyd HA, Miyamoto K. The salience of visuospatial and organizational skills in reproducing the Rey-Osterreith Complex Figure in subjects with high and low IQs. Clin Neuropsychol. 2000;14(4):551–4. https://doi.org/10.1076/clin.14.4.551.7206.

Rey A, Ferrara-Mori G. appliquée Cdp. Reattivo della figura complessa: Manuale: Organizzazi*oni speciali; 1967.

Shulman KI. Clock-drawing: is it the ideal cognitive screening test? Int J Geriatr Psychiatry. 2000;15(6):548–61. https://doi.org/10.1002/1099-1166(200006)15:6<548::AID-GPS242>3.0.CO;2-U.

Freedman M, Leach L, Kaplan E, Winocur G, Shulman KI, Delis DC. Clock drawing: A neuropsychological analysis. New York: Oxford University Press; 1994. p. vi. 182-vi

Kim S, Jahng S, Yu K-H, Lee B-C, Kang Y. Usefulness of the clock drawing test as a cognitive screening instrument for mild cognitive impairment and mild dementia: an evaluation using three scoring systems. Dement Neurocogn Disord. 2018;17(3):100–9. https://doi.org/10.12779/dnd.2018.17.3.100.

Allone C, Lo Buono V, Corallo F, Bonanno L, Palmeri R, Di Lorenzo G, et al. Cognitive impairment in Parkinson’s disease, Alzheimer’s dementia, and vascular dementia: the role of the clock-drawing test. Psychogeriatrics. 2018;18(2):123–31. https://doi.org/10.1111/psyg.12294.

Lee KS, Kim EA, Hong CH, Lee DW, Oh BH, Cheong HK. Clock drawing test in mild cognitive impairment: quantitative analysis of four scoring methods and qualitative analysis. Dement Geriatr Cogn Disord. 2008;26(6):483–9. https://doi.org/10.1159/000167879.

Muller S, Herde L, Preische O, Zeller A, Heymann P, Robens S, et al. Diagnostic value of digital clock drawing test in comparison with CERAD neuropsychological battery total score for discrimination of patients in the early course of Alzheimer’s disease from healthy individuals. Sci Rep. 2019;9(1):3543. https://doi.org/10.1038/s41598-019-40010-0.

Lee KS, Cheong H-K, Oh BH, Hong CH, Lee D-W. Reliability and validity of four scoring methods of clock drawing test for screening dementia and mild cognitive impairment. Dement Neurocogn Disord. 2009;53.

Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA, Foster NL, Jack CR Jr, Galasko DR, Doody R, Kaye J, Sano M, Mohs R, Gauthier S, Kim HT, Jin S, Schultz AN, Schafer K, Mulnard R, van Dyck C, Mintzer J, Zamrini EY, Cahn-Weiner D, Thal LJ, Alzheimer's Disease Cooperative Study. Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol. 2004;61(1):59–66. https://doi.org/10.1001/archneur.61.1.59.

O’Bryant SE, Waring SC, Cullum CM, Hall J, Lacritz L, Massman PJ, et al. Staging dementia using clinical dementia rating scale sum of boxes scores: a Texas Alzheimer’s research consortium study. Arch Neurol. 2008;65(8):1091–5. https://doi.org/10.1001/archneur.65.8.1091.

Davis R, Libon DJ, Au R, Pitman D, Penney DL. THink: inferring cognitive status from subtle behaviors. Proc Conf AAAI Artif Intell. 2014;2014:2898–905.

Souillard-Mandar W, Davis R, Rudin C, Au R, Libon DJ, Swenson R, Price CC, Lamar M, Penney DL. Learning classification models of cognitive conditions from subtle behaviors in the digital clock drawing test. Mach Learn. 2016;102(3):393–441. https://doi.org/10.1007/s10994-015-5529-5.

Rampasek L, Goldenberg A. TensorFlow: biology’s gateway to deep learning? Cell Syst. 2016;2(1):12–4. https://doi.org/10.1016/j.cels.2016.01.009.

Lee S-Y, Yoon S-Y, Kim M-J, Rhee HY, Ryu C-W, Jahng G-H. Investigation of the correlation between Seoul neuropsychological screening battery scores and the gray matter volume after correction of covariates of the age, gender, and genotypes in patients with AD and MCI. J Korean Soc Magn Reson Med. 2013;17(4):294–307. https://doi.org/10.13104/jksmrm.2013.17.4.294.

Park SY, Byun BH, Kim BI, Lim SM, Ko IO, Lee KC, Kim KM, Kim YK, Lee JY, Bu SH, Kim JH, Chi DY, Ha JH. The correlation of neuropsychological evaluation with 11C-PiB and 18F-FC119S amyloid PET in mild cognitive impairment and Alzheimer disease. Medicine (Baltimore). 2020;99(16):e19620. https://doi.org/10.1097/MD.0000000000019620.

Rosselli M, Ardila A. Effects of age, education, and gender on the Rey-osterrieth complex figure. Clin Neuropsychol. 1991;5(4):370–6. https://doi.org/10.1080/13854049108404104.

Boone KB, Lesser IM, Hill-gutierrez E, Berman NG, D'Elia LF. Rey-osterrieth complex figure performance in healthy, older adults: relationship to age, education, sex, and IQ. Clin Neuropsychol. 1993;7(1):22–8. https://doi.org/10.1080/13854049308401884.

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