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Authors: Nicolas Gomes 1 ; Arissa Yoshida 1 ; Mateus Roder 1 ; Guilherme Camargo de Oliveira 1 ; 2 and João Paulo Papa 1

Affiliations: 1 Department of Computing, São Paulo State University (UNESP), Brazil ; 2 School of Engineering, Royal Melbourne Institute of Technology (RMIT), Australia

Keyword(s): Neurodegenerative Disease, ALS, Graph Neural Networks, Facial Point Graph.

Abstract: Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is essential for establishing the beginning of treatment, enriching the outlook, and enhancing the overall well-being of those affected individuals. However, early diagnosis and detecting the disease’s signs is not straightforward. A simpler and cheaper way arises by analyzing the patient’s facial expressions through computational methods. When a patient with ALS engages in specific actions, e.g., opening their mouth, the movement of specific facial muscles differs from that observed in a healthy individual. This paper proposes Facial Point Graphs to learn information from the geometry of facial images to identify ALS automatically. The experimental outcomes in the Toronto Neuroface dataset show the proposed approach outperformed state-of-the-art results, fostering promising developments in the area.

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Paper citation in several formats:
Gomes, N.; Yoshida, A.; Roder, M.; Camargo de Oliveira, G. and Paulo Papa, J. (2024). Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 207-214. DOI: 10.5220/0012428400003660

@conference{visapp24,
author={Nicolas Gomes. and Arissa Yoshida. and Mateus Roder. and Guilherme {Camargo de Oliveira}. and João {Paulo Papa}.},
title={Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012428400003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
SN - 978-989-758-679-8
IS - 2184-4321
AU - Gomes, N.
AU - Yoshida, A.
AU - Roder, M.
AU - Camargo de Oliveira, G.
AU - Paulo Papa, J.
PY - 2024
SP - 207
EP - 214
DO - 10.5220/0012428400003660
PB - SciTePress

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