Computerised respiratory sounds can differentiate smokers and non-smokers

Oliveira, Ana; Şen, İpek; Kahya, Yasemin P.; Afreixo, Vera; Marques, Alda

doi:10.1007/s10877-016-9887-8

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Number of crackles and wheeze occupation rate acquired in posterior locations have been shown to be valid against lung function (-0.11<rs<-0.44) 28 , reliable (0.25<ICC1,2<0.86) 28,29 and sensitive to changes in patients with stable and exacerbated COPD 15,30 . Further details on respiratory sound acquisition and analysis have been provided elsewhere 31 .…”

Section: Data Collectionmentioning

confidence: 99%

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†

Oliveira

Machado

Marques

2018

COPD: Journal of Chronic Obstructive Pulmonary Disease

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Interpreting clinical changes during acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is challenging due to the absence of established minimal detectable (MDD) and important (MID) differences for most respiratory measures. This study established MDD and MID for respiratory measures in outpatients with AECOPD following pharmacological treatment. COPD assessment test (CAT), modified Borg scale (MBS), modified British Medical Research Council questionnaire (mMRC), peripheral oxygen saturation (SpO2), computerised respiratory sounds and forced expiratory volume in one second (FEV1) were collected within 24-48h of an AECOPD and after 45 days of pharmacological treatment. MID and MDD were calculated using anchor-(ROC and linear regression analysis) and distribution-based methods (effect size, SEM, 0.5*SD and MDC95) and pooled using Meta XL. Forty-four outpatients with AECOPD (31♂; 68.2±9.1yrs; FEV1 51.1±20.3%predicted) participated. Significant correlations with CAT were found for the MBS (r=0.34), mMRC (r=0.39) and FEV1 (r=0.33), resulting in MIDs of 0.

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Section: Data Collectionmentioning

confidence: 99%

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†

Oliveira

Machado

Marques

2018

COPD: Journal of Chronic Obstructive Pulmonary Disease

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“…Background noise was closely superimposed to respiratory sound intensity at lateral chest; hence, these locations were excluded from further analyses. The average spectra of normal respiratory sounds at trachea, anterior, lateral and posterior chest can be found in the Supporting Information and a detailed description of the signal processing is provided elsewhere …”

Section: Methodsmentioning

confidence: 99%

“…The average spectra of normal respiratory sounds at trachea, anterior, lateral and posterior chest can be found in the Supporting Information and a detailed description of the signal processing is provided elsewhere. 28…”

Section: Signal Processingmentioning

confidence: 99%

Reliability, validity and minimal detectable change of computerized respiratory sounds in patients with chronic obstructive pulmonary disease

Oliveira

Lage

Rodrigues

et al. 2017

Clinical Respiratory J

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“…Smoking and AI Significant differences between digitally recorded respiratory sounds of healthy smokers and non-smokers have been noticed and can be used as the earliest indicator for detecting smoking-related respiratory diseases such as COPD, lung cancer, etc. [97].…”

Section: Cystic Fibrosismentioning

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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Computerised respiratory sounds can differentiate smokers and non-smokers

Cited by 6 publications

References 35 publications

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†

Reliability, validity and minimal detectable change of computerized respiratory sounds in patients with chronic obstructive pulmonary disease

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

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Computerised respiratory sounds can differentiate smokers and non-smokers

Cited by 6 publications

References 35 publications

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD†

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD†

Reliability, validity and minimal detectable change of computerized respiratory sounds in patients with chronic obstructive pulmonary disease

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

Contact Info

Product

Resources

About

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†

Minimal Important and Detectable Differences of Respiratory Measures in Outpatients with AECOPD^†