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Detecting Dementia from Face-Related Features with Automated Computational Methods
Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376259/ https://www.ncbi.nlm.nih.gov/pubmed/37508889 http://dx.doi.org/10.3390/bioengineering10070862 |
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author | Zheng, Chuheng Bouazizi, Mondher Ohtsuki, Tomoaki Kitazawa, Momoko Horigome, Toshiro Kishimoto, Taishiro |
author_facet | Zheng, Chuheng Bouazizi, Mondher Ohtsuki, Tomoaki Kitazawa, Momoko Horigome, Toshiro Kishimoto, Taishiro |
author_sort | Zheng, Chuheng |
collection | PubMed |
description | Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection. |
format | Online Article Text |
id | pubmed-10376259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103762592023-07-29 Detecting Dementia from Face-Related Features with Automated Computational Methods Zheng, Chuheng Bouazizi, Mondher Ohtsuki, Tomoaki Kitazawa, Momoko Horigome, Toshiro Kishimoto, Taishiro Bioengineering (Basel) Article Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection. MDPI 2023-07-20 /pmc/articles/PMC10376259/ /pubmed/37508889 http://dx.doi.org/10.3390/bioengineering10070862 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zheng, Chuheng Bouazizi, Mondher Ohtsuki, Tomoaki Kitazawa, Momoko Horigome, Toshiro Kishimoto, Taishiro Detecting Dementia from Face-Related Features with Automated Computational Methods |
title | Detecting Dementia from Face-Related Features with Automated Computational Methods |
title_full | Detecting Dementia from Face-Related Features with Automated Computational Methods |
title_fullStr | Detecting Dementia from Face-Related Features with Automated Computational Methods |
title_full_unstemmed | Detecting Dementia from Face-Related Features with Automated Computational Methods |
title_short | Detecting Dementia from Face-Related Features with Automated Computational Methods |
title_sort | detecting dementia from face-related features with automated computational methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376259/ https://www.ncbi.nlm.nih.gov/pubmed/37508889 http://dx.doi.org/10.3390/bioengineering10070862 |
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