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 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.

A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital

“…In this study BS were recorded from COVID-19 patients at peak disease (PD) and pre-discharge from hospital (D). Acquiring BS from COVID-19 patients in a quarantine ward was easy to implement as previously reported [12] , [13] and BSR can be auscultated in real-time with the device and various analyses can be performed off-line. The VoqX TM device helps overcoming the standard auscultation limitations imposed by quarantine conditions.…”

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

“…An alarming 80% of COPD deaths occur in low- and middle-income countries (LMICs), where the lack of accessibility to health care treatment and affordability for current medical devices limits the feasibility of tracking the development of these progressive diseases over extended periods. For the diagnosis and care of patients with COPD and CVD, accurate auscultation is helpful for the diagnosis at an early stage and evaluation of the treatment response ( 4 , 5 ). Wheezing is important in the diagnosis and monitoring of those diseases ( 6 ).…”

“…Wheezing is important in the diagnosis and monitoring of those diseases ( 6 ). Crackles have a crucial significance in diagnosing pneumonia, idiopathic pulmonary fibrosis, and pulmonary edema ( 5 ). Stridor suggests severe obstruction of the upper airway and is helpful for the patient’s emergency treatment.…”

“…In addition, analysis of auscultation sounds is quite different depending on the knowledge and experience of the clinician. As a result, some critical respiratory or heart diseases are underdiagnosed or misdiagnosed ( 5 ). Recently, the incidence and socioeconomic burden of COPD and CVD continue to increase because of the increasingly aging population, worsening air pollution, and various infectious diseases.…”

Fractal and Time-series A nalyses based Rhonchi and Bronchial Auscultation: A Machine Learning Approach

“…Rhonchi is a continuous, low-pitched, adventitious lung sound that is heard in bronchi during both the inspiratory and expiratory lung cycle (3) , which indicates COPD, pneumonia, bronchiectasis, and bronchitis (4,5) . For auscultation and biomedical imaging, several mathematical tools like Fourier transform and wavelet transform have been employed (6) .…”