Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning

Abstract Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of r...

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Autores principales:	Yoonjoo Kim, YunKyong Hyon, Sung Soo Jung, Sunju Lee, Geon Yoo, Chaeuk Chung, Taeyoung Ha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/350a496dc10741ddb83ca44fafc5d08c
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spelling	oai:doaj.org-article:350a496dc10741ddb83ca44fafc5d08c2021-12-02T15:09:16ZRespiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning10.1038/s41598-021-96724-72045-2322https://doaj.org/article/350a496dc10741ddb83ca44fafc5d08c2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96724-7https://doaj.org/toc/2045-2322Abstract Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician’s considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.Yoonjoo KimYunKyong HyonSung Soo JungSunju LeeGeon YooChaeuk ChungTaeyoung HaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yoonjoo Kim YunKyong Hyon Sung Soo Jung Sunju Lee Geon Yoo Chaeuk Chung Taeyoung Ha Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
description	Abstract Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician’s considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
format	article
author	Yoonjoo Kim YunKyong Hyon Sung Soo Jung Sunju Lee Geon Yoo Chaeuk Chung Taeyoung Ha
author_facet	Yoonjoo Kim YunKyong Hyon Sung Soo Jung Sunju Lee Geon Yoo Chaeuk Chung Taeyoung Ha
author_sort	Yoonjoo Kim
title	Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
title_short	Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
title_full	Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
title_fullStr	Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
title_full_unstemmed	Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
title_sort	respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/350a496dc10741ddb83ca44fafc5d08c
work_keys_str_mv	AT yoonjookim respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT yunkyonghyon respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT sungsoojung respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT sunjulee respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT geonyoo respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT chaeukchung respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning AT taeyoungha respiratorysoundclassificationforcrackleswheezesandrhonchiintheclinicalfieldusingdeeplearning
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Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning

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