A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition

Background: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low risk of droplet transmission for sarcopenic dysphag...

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Autores principales:	Kotomi Sakai, Stuart Gilmour, Eri Hoshino, Enri Nakayama, Ryo Momosaki, Nobuo Sakata, Daisuke Yoneoka
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dysphagia sarcopenia screening image recognition Nutrition. Foods and food supply TX341-641
Acceso en línea:	https://doaj.org/article/1ec08c29905d49afa5ed3458492a6051
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spelling	oai:doaj.org-article:1ec08c29905d49afa5ed3458492a60512021-11-25T18:36:02ZA Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition10.3390/nu131140092072-6643https://doaj.org/article/1ec08c29905d49afa5ed3458492a60512021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6643/13/11/4009https://doaj.org/toc/2072-6643Background: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low risk of droplet transmission for sarcopenic dysphagia. Methods: Older patients admitted to a post-acute care hospital were enrolled in this cross-sectional study. As a main variable for the development of a screening test, we photographed the anterior neck to analyze the image features of sarcopenic dysphagia. The studied image features included the pixel values and the number of feature points. We constructed screening models using the image features, age, sex, and body mass index. The prediction performance of each model was investigated. Results: A total of 308 patients participated, including 175 (56.82%) patients without dysphagia and 133 (43.18%) with sarcopenic dysphagia. The area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, positive predictive value, negative predictive value, and area under the precision-recall curve (PR-AUC) values of the best model were 0.877, 87.50%, 76.67%, 66.67%, 92.00%, and 0.838, respectively. The model with image features alone showed an ROC-AUC of 0.814 and PR-AUC of 0.726. Conclusions: The screening test for sarcopenic dysphagia using image recognition of neck appearance had high prediction performance.Kotomi SakaiStuart GilmourEri HoshinoEnri NakayamaRyo MomosakiNobuo SakataDaisuke YoneokaMDPI AGarticledysphagiasarcopeniascreeningimage recognitionNutrition. Foods and food supplyTX341-641ENNutrients, Vol 13, Iss 4009, p 4009 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dysphagia sarcopenia screening image recognition Nutrition. Foods and food supply TX341-641
spellingShingle	dysphagia sarcopenia screening image recognition Nutrition. Foods and food supply TX341-641 Kotomi Sakai Stuart Gilmour Eri Hoshino Enri Nakayama Ryo Momosaki Nobuo Sakata Daisuke Yoneoka A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
description	Background: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low risk of droplet transmission for sarcopenic dysphagia. Methods: Older patients admitted to a post-acute care hospital were enrolled in this cross-sectional study. As a main variable for the development of a screening test, we photographed the anterior neck to analyze the image features of sarcopenic dysphagia. The studied image features included the pixel values and the number of feature points. We constructed screening models using the image features, age, sex, and body mass index. The prediction performance of each model was investigated. Results: A total of 308 patients participated, including 175 (56.82%) patients without dysphagia and 133 (43.18%) with sarcopenic dysphagia. The area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, positive predictive value, negative predictive value, and area under the precision-recall curve (PR-AUC) values of the best model were 0.877, 87.50%, 76.67%, 66.67%, 92.00%, and 0.838, respectively. The model with image features alone showed an ROC-AUC of 0.814 and PR-AUC of 0.726. Conclusions: The screening test for sarcopenic dysphagia using image recognition of neck appearance had high prediction performance.
format	article
author	Kotomi Sakai Stuart Gilmour Eri Hoshino Enri Nakayama Ryo Momosaki Nobuo Sakata Daisuke Yoneoka
author_facet	Kotomi Sakai Stuart Gilmour Eri Hoshino Enri Nakayama Ryo Momosaki Nobuo Sakata Daisuke Yoneoka
author_sort	Kotomi Sakai
title	A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
title_short	A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
title_full	A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
title_fullStr	A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
title_full_unstemmed	A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition
title_sort	machine learning-based screening test for sarcopenic dysphagia using image recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1ec08c29905d49afa5ed3458492a6051
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A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition

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