Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test tha...

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Autores principales:	Youngbeen Chung, Jie Jin, Hyun In Jo, Hyun Lee, Sang-Heon Kim, Sung Jun Chung, Ho Joo Yoon, Junhong Park, Jin Yong Jeon
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cough pneumonia machine-learning artificial intelligence long short-term memory loudness Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e1bd8b0cbe2142338ca9e346e04c6b9b
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spelling	oai:doaj.org-article:e1bd8b0cbe2142338ca9e346e04c6b9b2021-11-11T19:03:59ZDiagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI10.3390/s212170361424-8220https://doaj.org/article/e1bd8b0cbe2142338ca9e346e04c6b9b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7036https://doaj.org/toc/1424-8220Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.Youngbeen ChungJie JinHyun In JoHyun LeeSang-Heon KimSung Jun ChungHo Joo YoonJunhong ParkJin Yong JeonMDPI AGarticlecoughpneumoniamachine-learningartificial intelligencelong short-term memoryloudnessChemical technologyTP1-1185ENSensors, Vol 21, Iss 7036, p 7036 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cough pneumonia machine-learning artificial intelligence long short-term memory loudness Chemical technology TP1-1185
spellingShingle	cough pneumonia machine-learning artificial intelligence long short-term memory loudness Chemical technology TP1-1185 Youngbeen Chung Jie Jin Hyun In Jo Hyun Lee Sang-Heon Kim Sung Jun Chung Ho Joo Yoon Junhong Park Jin Yong Jeon Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
description	Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
format	article
author	Youngbeen Chung Jie Jin Hyun In Jo Hyun Lee Sang-Heon Kim Sung Jun Chung Ho Joo Yoon Junhong Park Jin Yong Jeon
author_facet	Youngbeen Chung Jie Jin Hyun In Jo Hyun Lee Sang-Heon Kim Sung Jun Chung Ho Joo Yoon Junhong Park Jin Yong Jeon
author_sort	Youngbeen Chung
title	Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
title_short	Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
title_full	Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
title_fullStr	Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
title_full_unstemmed	Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
title_sort	diagnosis of pneumonia by cough sounds analyzed with statistical features and ai
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e1bd8b0cbe2142338ca9e346e04c6b9b
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Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

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