Lung sounds in asbestos induced pulmonary disorders

Piirilä, Päivi; Lehtola, Hannu; Zitting, Antti; Kivisaari, Leena; Koskinen, Heikki; Luukkonen, Ritva; Salo, Sirpa; Vehmas, Tapio; Nordman, Henrik; Sovijärvi, Anssi

doi:10.1183/09031936.00.16590100

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…Th e frequency of normal respiratory sounds was found to be higher during inspiration than expiration (17). Th is fi nding is in line with previous literature describing the normal respiratory sounds of healthy people (26) and of people with chronic diseases, such as bronchiectasis, fi brosing alveolitis and asbestos-related pleural disease (17,27). Normal respiratory sounds at the trachea presented higher frequencies than sounds at the posterior chest.…”

Section: Discussionsupporting

confidence: 88%

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

Jácome

Marques

2014

COPD: Journal of Chronic Obstructive Pulmonary Disease

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Computerized respiratory sound analysis provides objective information about the respiratory system and may be useful to monitor patients with chronic obstructive pulmonary disease (COPD) and detect exacerbations early. For these purposes, a thorough understanding of the typical computerized respiratory sounds in patients with COPD during stable periods is essential. This review aimed to systematize the existing evidence on computerized respiratory sounds in stable COPD. A literature search in the Medline, EBSCO, Web of Knowledge and Scopus databases was performed. Seven original articles were included. The maximum frequencies of normal inspiratory sounds at the posterior chest were between 113 and 130Hz, lower than the frequency found at trachea (228 Hz). During inspiration, the frequency of normal respiratory sounds was found to be higher than expiration (130 vs. 100Hz). Crackles were predominantly inspiratory (2.9-5 vs. expiratory 0.73-2) and characterized by long durations of the variables initial deflection width (1.88-2.1 ms) and two cycle duration (7.7-11.6 ms). Expiratory wheeze rate was higher than inspiratory rate. In patients with COPD normal respiratory sounds seem to follow the pattern observed in healthy people and adventitious respiratory sounds are mainly characterized by inspiratory and coarse crackles and expiratory wheezes. Further research with larger samples and following the Computerized Respiratory Sound Analysis (CORSA) guidelines are needed.

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Section: Discussionsupporting

confidence: 88%

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

Jácome

Marques

2014

COPD: Journal of Chronic Obstructive Pulmonary Disease

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“…British investigators have reported that detecting crackles on time-expanded waveform analysis was equivalent to CT scans in detecting asbestosis [14]. Finnish investigators also showed a significant positive correlation with frequencies of lung sounds and pulmonary fibrosis detected on HRCT [15]. Of course in industrial settings, in contrast to ER's and ICU's, neither CHF nor PN crackles are likely to be confounding variables.…”

Section: Discussionmentioning

confidence: 99%

Automated Analysis of Crackles in Patients with Interstitial Pulmonary Fibrosis

et al. 2011

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Background. The crackles in patients with interstitial pulmonary fibrosis (IPF) can be difficult to distinguish from those heard in patients with congestive heart failure (CHF) and pneumonia (PN). Misinterpretation of these crackles can lead to inappropriate therapy. The purpose of this study was to determine whether the crackles in patients with IPF differ from those in patients with CHF and PN. Methods. We studied 39 patients with IPF, 95 with CHF and 123 with PN using a 16-channel lung sound analyzer. Crackle features were analyzed using machine learning methods including neural networks and support vector machines. Results. The IPF crackles had distinctive features that allowed them to be separated from those in patients with PN with a sensitivity of 0.82, a specificity of 0.88 and an accuracy of 0.86. They were separated from those of CHF patients with a sensitivity of 0.77, a specificity of 0.85 and an accuracy of 0.82. Conclusion. Distinctive features are present in the crackles of IPF that help separate them from the crackles of CHF and PN. Computer analysis of crackles at the bedside has the potential of aiding clinicians in diagnosing IPF more easily and thus helping to avoid medication errors.

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“…Frequency distribution of lung sounds using computerized lung sound analyzer is significantly associated with interstitial lung fibrosis on high-resolution computed tomography (HRCT) scoring in patients with asbestos-related lung injury. The inspiratory crackles and high sound frequencies are associated with fibrotic changes to the lung while low sound frequencies were associated with emphysematous components of the asbestos-injured lung [ 91 ].…”

Section: Clinical Applications Of Lung Soundsmentioning

confidence: 99%

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Sethi

Muddaloor

Anvekar

et al. 2023

Sensors

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Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

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Lung sounds in asbestos induced pulmonary disorders

Cited by 11 publications

References 24 publications

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

Automated Analysis of Crackles in Patients with Interstitial Pulmonary Fibrosis

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

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