Alzheimer’s Disease; Deep Learning; House Drawing; Mild Cognitive Impairment; Off-Line; On-Line; Alzheimer; Alzheimer’s disease; Cognitive impairment; Deep learning; Healthy controls; House drawing; Machine learning algorithms; Mild cognitive impairment; Off-line; On-line; Computer Networks and Communications; Signal Processing; Software
Abstract :
[en] Objective: Evaluate the effectiveness of machine learning (ML) algorithms in classifying mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using features derived from the House Drawing Test (HDT). Methods: The HDT was administered to 58 participants, categorized into AD (n = 22), MCI (n= 25), and Healthy Controls (HC, n = 11). Drawings were simultaneously captured using an electronic pen (on-line format) and scanned (off-line format). Results: The models achieved high classification accuracy across all groups: HC vs. MCI (67%), MCI vs. AD (70%), HC vs. AD (76%). Our results showcase the potential of ML for early screening of neurodegenerative diseases.
Disciplines :
Computer science
Author, co-author :
Hosseini-Kivanani, Nina; University of Luxembourg, Luxembourg
Salobrar-García, Elena; Ramon Castroviejo Institute of Ophthalmologic Research, Spain ; Universidad Complutense of Madrid, Spain
Elvira-Hurtado, Lorena; Universidad Complutense of Madrid, Spain ; Memory Unit, Geriatrics Service, Hospital Clínico San Carlos, Spain
Salas, Mario
SCHOMMER, Christoph ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
LEIVA, Luis A. ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
yes
Language :
English
Title :
SCREENING OF ALZHEIMER’S DISEASE AND MILD COGNITIVE IMPAIRMENT THROUGH INTEGRATED ON-LINE AND OFF-LINE HOUSE DRAWING TESTS
Publication date :
2024
Event name :
Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024
Event place :
Zagreb, Hrv
Event date :
26-10-2024 => 28-10-2024
Audience :
International
Main work title :
Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024
Editor :
Miranda, Paula
Publisher :
IADIS Press
ISBN/EAN :
9789898704627
Peer reviewed :
Peer reviewed
European Projects :
HE - 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making
FnR Project :
FNR15722813 - BANANA - Brainsourcing For Affective Attention Estimation, 2021 (01/02/2022-31/01/2025) - Luis Leiva
Funders :
European Union
Funding text :
Work supported by the UCM research group (Grupo de Investigaci\u00F3n b\u00E1sica en Ciencias de la Visi\u00F3n del IIORC, UCM-GR17-920105), the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001), and the European Innovation Council Pathfinder program (grant 101071147).
Bensalah, A., Parziale, A., De Gregorio, G., Marcelli, A., Fornés, A., & Lladós, J. (2023). I can’t believe it’s not better: In-air movement for Alzheimer handwriting synthetic generation. In Proceedings of IGS.
Chan, J. Y., Bat, B. K., Wong, A., Chan, T. K., Huo, Z., Yip, B. H., & Tsoi, K. K. (2021). Evaluation of digital drawing tests and paper-and-pencil drawing tests for the screening of mild cognitive impairment and dementia: A systematic review and meta-analysis of diagnostic studies. Neuropsychology Review, 31(4).
Cheah, W.-T., Chang, W.-D., Hwang, J.-J., Hong, S.-Y., Fu, L.-C., & Chang, Y.-L. (2019). A screening system for mild cognitive impairment based on neuropsychological drawing test and neural network. In Proceedings of IEEE SMC.
Chen, H.-Y., Li, D.-C., & Lin, L.-S. (2016). Extending sample information for small data set prediction. In Proceedings of IIAI-AAI.
Chen, S., Stromer, D., Alnasser Alabdalrahim, H., Schwab, S., Weih, M., & Maier, A. (2020). Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Scientific Reports, 10(1).
Cho, K., Van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of EMNLP.
Cilia, N., D’Alessandro, T., De Stefano, C., & Fontanella, F. (2022). Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction. Machine Vision and Applications, 33.
Cilia, N. D., D’Alessandro, T., De Stefano, C., Fontanella, F., & Molinara, M. (2021). From online handwriting to synthetic images for Alzheimer’s disease detection using a deep transfer learning approach. IEEE Journal of Biomedical and Health Informatics, 25(12).
Ding, Z., Lee, T., & Chan, A. (2022). Digital cognitive biomarker for mild cognitive impairments and dementia: A systematic review. Journal of Clinical Medicine, 11.
Garre-Olmo, J., Faúndez-Zanuy, M., López-de Ipiña, K., Calvó-Perxas, L., & Turró-Garriga, O. (2017). Kinematic and pressure features of handwriting and drawing: Preliminary results between patients with mild cognitive impairment, Alzheimer’s disease, and healthy controls. Current Alzheimer Research, 14(9).
Ghaderyan, P., Abbasi, A., & Saber, S. (2018). A new algorithm for kinematic analysis of handwriting data; Towards a reliable handwriting-based tool for early detection of Alzheimer’s disease. Expert Systems with Applications, 114.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of CVPR.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8).
Hosseini-Kivanani, N., Salobrar-García, E., Elvira-Hurtado, L., López-Cuenca, I., de Hoz, R., Ramírez, J. M., Gil, P., Salas, M., Schommer, C., & Leiva, L. A. (2024). Ink of insight: Data augmentation for dementia screening through deep learning. In Proceedings of ICMHI, Japan, Yokohama.
Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of CVPR.
Impedovo, D., & Pirlo, G. (2018). Dynamic handwriting analysis for the assessment of neurodegenerative diseases: A pattern recognition perspective. IEEE Reviews in Biomedical Engineering, 12.
Kim, K. W., Lee, S. Y., Choi, J., Chin, J., Lee, B. H., Na, D. L., & Choi, J. H. (2020). A comprehensive evaluation of the process of copying a complex figure in early-and late-onset Alzheimer’s disease: A quantitative analysis of digital pen data. Journal of Medical Internet Research, 22(8), e18136.
Knechtl, P., & Lehrner, J. (2023). Visuoconstructional abilities of patients with subjective cognitive decline, mild cognitive impairment, and Alzheimer’s disease. Journal of Geriatric Psychiatry and Neurology.
Kobayashi, M., Yamada, Y., Shinkawa, K., Nemoto, M., Nemoto, K., & Arai, T. (2022). Automated early detection of Alzheimer’s disease by capturing impairments in multiple cognitive domains with multiple drawing tasks. Journal of Alzheimer’s Disease, 88(3).
Liss, J. L., Seleri Assunção, S., Cummings, J., Atri, A., Geldmacher, D. S., Candela, S. F., & Sabbagh, M. N. (2021). Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer’s disease (MCI and dementia) in primary care: A review and synthesis. Journal of Internal Medicine, 290(2).
Maslych, M., Taranta, E. M., Aldilati, M., & Laviola, J. J. (2023). Effective 2D stroke-based gesture augmentation for RNNs. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI’23).
Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621.
Poreh, A., Levin, J. B., & Teaford, M. (2020). Geriatric complex figure test: A test for the assessment of planning, visual spatial ability, and memory in older adults. Applied Neuropsychology: Adult, 27(2), 101–107.
Rouleau, I., Salmon, D. P., & Butters, N. (1996). Longitudinal analysis of clock drawing in Alzheimer’s disease patients. Brain and Cognition, 31(1).
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1).
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of ICML.
Trojano, L., & Gainotti, G. (2016). Drawing disorders in Alzheimer’s disease and other forms of dementia. Journal of Alzheimer’s Disease, 53(1).
Tsatali, M., Avdikou, K., Gialaouzidis, M., Minopoulou, D., Emmanouel, A., Kouroundi, E., & Tsolaki, M. (2022). The discriminant validity of Rey complex figure test (RCFT) in subjective cognitive decline, mild cognitive impairment, and Alzheimer’s disease dementia in Greek older adults. Applied Neuropsychology: Adult.
Werner, P., Rosenblum, S., Baron, G., Heinik, J., & Korczyn, A. (2006). Handwriting process variables discriminating mild Alzheimer’s disease and mild cognitive impairment. Journal of Gerontology Series B, 61(4).
Wimo, A., Seeher, K., Cataldi, R., Cyhlarova, E., Dielemann, J. L., Frisell, O., & Dua, T. (2023). The worldwide costs of dementia in 2019. Alzheimer’s & Dementia, 19(7).
Xu, F., Ding, Y., Ling, Z., Li, X., Li, Y., & Wang, S. (2020). DCDT: A digital clock drawing test system for cognitive impairment screening. In Proceedings of ICDE.
Young, Y. C., Pyun, J. M., Ryu, N., Baek, M. J., Jang, J. W., Park, Y. H., Ahn, S. W., Shin, H.-W., Park, K.-Y., & Kim, S. Y. (2021). Use of the clock drawing test and the Rey–Osterrieth complex figure test-copy with convolutional neural networks to predict cognitive impairment. Alzheimer’s Research & Therapy.
Zhang, X., Zhao, Y., Lv, L., Min, G., Wang, Q., & Li, Y. (2021). A study on the performance characteristics and diagnostic efficacy of digital clock drawing test in patients with amnesic mild cognitive impairment. Chinese Journal of Behavioral Medicine and Brain Science, 30.
Öhman, F., Hassenstab, J., Berron, D., Schöll, M., & Papp, K. V. (2021). Current advances in digital cognitive assessment for preclinical Alzheimer’s disease. DADM, 13(1).
