[en] Alzheimer’s disease (AD) is the leading cause of dementia. Although there is currently no cure for AD, early detection of cognitive decline can help clinicians mitigate its impact. Recently, Machine Learning (ML) approaches have been developed to automatically analyze handwriting and hand-drawing tasks to support the early diagnosis of AD. In this paper, we study pentagon and clock drawing tests using both off-line (scanned image pixels) and on-line (discrete point sequences) data as input to several ML models (i.e., DensNet, ResNet, EfficientNet, RNN, LSTM, and GRU). Our study is the first to determine the most effective modality (on-line vs. off-line) and drawing tasks to distinguish healthy controls from AD patients (binary classification) as well as two stages of AD severity (multi-class classification). Our results suggest that, contrary to other domains, the off-line modality outperforms the on-line one, sometimes by a large margin: 90% vs. 60% accuracy in binary classification and 53% vs. 82% accuracy in multi-class classification. This suggests that, for drawing tasks and small-scale datasets, image-based representations may be more effective in predicting AD than those relying on more complex data representations.
Disciplines :
Computer science
Author, co-author :
HOSSEINI KIVANANI, Nina ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Salobrar-García, Elena; Ramon Castroviejo Institute of Ophthalmologic Research, Madrid, Spain ; Universidad Complutense of Madrid, Madrid, Spain
Elvira-Hurtado, Lorena; Universidad Complutense of Madrid, Madrid, Spain ; Memory Unit, Geriatrics Service, Hospital Clínico San Carlos, Madrid, Spain
Salas, Mario; Memory Unit, Geriatrics Service, Hospital Clínico San Carlos, Madrid, Spain
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 :
Blueprint of Tomorrow: Contrasting Off-Line and On-Line Drawing Tasks for Alzheimer’s Disease Screening
Publication date :
November 2024
Event name :
International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Event place :
Valencia, Esp
Event date :
20-11-2024 => 22-11-2024
Main work title :
Intelligent Data Engineering and Automated Learning – IDEAL 2024 - 25th International Conference, Proceedings
Editor :
Julian, Vicente
Publisher :
Springer Science and Business Media Deutschland GmbH
Work supported by the UCM research\u00A0group (Grupo de investigacion 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).
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