Innovation in Analysis of Respiratory Sounds

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

BACKGROUND

Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach.

METHODS

A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls.

DISCUSSION

This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022.

CLINICALTRIALS

gov Identifier: NCT05318599.

Chest Examination 3.0 With Wireless Technology in a Clinical Case Based on Literature Review

Physicians use auscultation as a standard method of thoracic examination: it is simple, reliable, non-invasive, and widely accepted. Artificial intelligence (AI) is the new frontier of thoracic examination as it makes it possible to integrate all available data (clinical, instrumental, laboratory, functional), allowing for objective assessments, precise diagnoses, and even the phenotypical characterization of lung diseases. Increasing the sensitivity and specificity of examinations helps provide tailored diagnostic and therapeutic indications, which also take into account the patient's clinical history and comorbidities. Several clinical studies, mainly conducted in children, have shown a good concordance between traditional and AI-assisted auscultation in detecting fibrotic diseases. On the other hand, the use of AI for the diagnosis of obstructive pulmonary disease is still debated as it gave inconsistent results when detecting certain types of lung noises, such as wet and dry crackles. Therefore, the application of AI in clinical practice needs further investigation. In particular, the pilot case report aims to address the use of this technology in restrictive lung disease, which in this specific case is pulmonary sarcoidosis. In the case we present, data integration allowed us to make the right diagnosis, avoid invasive procedures, and reduce the costs for the national health system; we show that integrating technologies can improve the diagnosis of restrictive lung disease. Randomized controlled trials will be needed to confirm the conclusions of this preliminary work.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Usefulness of new acoustic respiratory sound monitoring with artificial intelligence for upper airway assessment in obese patients during monitored anesthesia care

Monitored anesthesia care (MAC) often causes airway complications, particularly posing an elevated risk of aspiration and airway obstruction in obese patients. This study aimed to quantify the levels of aspiration and airway obstruction using an artificial intelligence (AI)-based acoustic analysis algorithm, assessing its utility in identifying airway complications in obese patients. To verify the correlation between the stridor quantitative value (STQV) calculated by acoustic analysis and body weight, and to further evaluate fluid retention and airway obstruction, STQV calculated exhaled breath sounds collected at the neck region, was compared before and after injection of 3 ml of water in the oral cavity and at the start and end of the MAC procedures. STQV measured immediately following the initiation of MAC exhibited a weak correlation with body mass index. Furhtermore, STQV values before and after water injection increased predominantly after injection, further increased at the end of MAC. AI-based analysis of cervical respiratory sounds can enhance the safety of airway management during MAC by quantifying airway obstruction and fluid retention in obese patients. J. Med. Invest. 70 : 430-435, August, 2023.

Non-invasive devices for respiratory sound monitoring

Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.

Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.

We present the literature review of non-invasive sound acquisition devices and techniques.

The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.

Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

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

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 machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study

Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the objective method for analyzing respiratory sounds including fine crackles is not clinically available. We have previously developed a machine-learning-based algorithm which can promptly analyze and quantify the respiratory sounds including fine crackles. In the present proof-of-concept study, we assessed the usefulness of fine crackles quantified by this algorithm in the diagnosis of ILDs.We evaluated the fine crackles quantitative values (FCQVs) in 60 participants who underwent high-resolution computed tomography (HRCT) and chest X-ray in our hospital. Right and left lung fields were evaluated separately.In sixty-seven lung fields with ILDs in HRCT, the mean FCQVs (0.121 ± 0.090) were significantly higher than those in the lung fields without ILDs (0.032 ± 0.023, P < .001). Among those with ILDs in HRCT, the mean FCQVs were significantly higher in those with idiopathic pulmonary fibrosis than in those with other types of ILDs (P = .002). In addition, the increased mean FCQV was associated with the presence of traction bronchiectasis (P = .003) and honeycombing (P = .004) in HRCT. Furthermore, in discriminating ILDs in HRCT, an FCQV-based determination of the presence or absence of fine crackles indicated a higher sensitivity compared to a chest X-ray-based determination of the presence or absence of ILDs.We herein report that the machine-learning-based quantification of fine crackles can predict the HRCT findings of lung fibrosis and can support the prompt and sensitive diagnosis of ILDs.

Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China

BACKGROUND

Effective auscultations are often hard to implement in isolation wards. To date, little is known about the characteristics of pulmonary auscultation in novel coronavirus (COVID-19) pneumonia.

OBJECTIVES

The aim of this study was to explore the features and clinical significance of pulmonary auscultation in COVID-19 pneumonia using an electronic stethoscope in isolation wards.

METHODS

This cross-sectional, observational study was conducted among patients with laboratory-confirmed COVID-19 at Wuhan Red-Cross Hospital during the period from January 27, 2020, to February 12, 2020. Standard auscultation with an electronic stethoscope was performed and electronic recordings of breath sounds were analyzed.

RESULTS

Fifty-seven patients with average age of 60.6 years were enrolled. The most common symptoms were cough (73.7%) during auscultation. Most cases had bilateral lesions (96.4%) such as multiple ground-glass opacities (69.1%) and fibrous stripes (21.8%). High-quality auscultation recordings (98.8%) were obtained, and coarse breath sounds, wheezes, coarse crackles, fine crackles, and Velcro crackles were identified. Most cases had normal breath sounds in upper lungs, but the proportions of abnormal breath sounds increased in the basal fields where Velcro crackles were more commonly identified at the posterior chest. The presence of fine and coarse crackles detected 33/39 patients with ground-glass opacities (sensitivity 84.6% and specificity 12.5%) and 8/9 patients with consolidation (sensitivity 88.9% and specificity 15.2%), while the presence of Velcro crackles identified 16/39 patients with ground-glass opacities (sensitivity 41% and specificity 81.3%).

CONCLUSIONS

The abnormal breath sounds in COVID-19 pneumonia had some consistent distributive characteristics and to some extent correlated with the radiologic features. Such evidence suggests that electronic auscultation is useful to aid diagnosis and timely management of the disease. Further studies are indicated to validate the accuracy and potential clinical benefit of auscultation in detecting pulmonary abnormalities in COVID-19 infection.

Desarrollo de una aplicación para configurar el teléfono inteligente como fonendoscopio para profesionales sanitarios con deficiencias auditivas

Introducción y objetivos. La hipoacusia supone un severo hándicap para cualquier profesional cuya actividad se base en el reconocimiento de sonidos. En el caso de profesionales sanitarios, la auscultación constituye una actividad rutinaria y el padecimiento de hipoacusia la limita en grado variable en función de la severidad de la misma. Aquellos profesionales sanitarios que por la severidad de su hipoacusia necesitan del uso de audífonos ven dificultadas las rutinas basadas en el uso del fonendoscopio. El objetivo del presente trabajo es describir el proceso llevado a cabo para desarrollar una aplicación para smartphones, que permita la reproducción en tiempo real, el registro y el análisis de sonidos para facilitar la labor de profesionales sanitarios con hipoacusia. Métodos. Hemos recogido somatosonidos cardiacos, pulmonares y abdominales de sujetos sanos y patológicos a fin de caracterizarles frecuencialmente. Posteriormente, la aplicación ha sido diseñada con el objetivo de facilitar la labor diagnóstica del profesional sanitario hipoacúsico, teniendo en cuenta la caracterización anterior para optimizar la escucha y el análisis de sonidos cardiacos, pulmonares y abdominales. Además, con el objetivo de maximizar el número de dispositivos compatibles, ha sido desarrollada para el sistema operativo Android, el más extendido del mercado. Resultados. Hemos desarrollado una App. para smartphones (a la que hemos llamado STETHOSCOPE) basados en Android que configura el teléfono como un fonendoscopio recogiendo el somatosonido a través de su micrófono (siendo posible utilizar exclusivamente el micrófono interno del smartphone o bien micrófonos externos de alta calidad a través de su conector JACK), procesando la señal hasta enviarla finalmente por Bluetooth a los audífonos del profesional hipoacúsico. Esta aplicación permite grabar y representar gráficamente sonidos cardiacos, pulmonares y abdominales en dispositivos Android y almacenarlos en formato WAV, según las recomendaciones del Instituto de Ingeniería Eléctrica y Electrónica (Institute of Electrical and Electronics Engineers, IEEE), utilizando una codificación FLOAT de 32 bits sin compresión posibilitando su archivo, comparación o compartición con otros profesionales. Conclusiones. En este estudio presentamos una aplicación destinada a utilizar el smartphone como fonendoscopio, haciendo llegar el sonido captado a la ayuda auditiva (por vía inalámbrica) del profesional sanitario hipoacúsico que lo precise.

The highs and lows of wheezing: A review of the most popular adventitious lung sound

Wheezing is the most widely reported adventitious lung sound in the English language. It is recognized by health professionals as well as by lay people, although often with a different meaning. Wheezing is an indicator of airway obstruction and therefore of interest particularly for the assessment of young children and in other situations where objective documentation of lung function is not generally available. This review summarizes our current understanding of mechanisms producing wheeze, its subjective perception and description, its objective measurement, and visualization, and its relevance in clinical practice.

Digital stethoscope: technology update

Cardiovascular disease (CVD) is recognized as the leading cause of mortality throughout the world. About one-third of global mortality is attributable to CVD. In addition to clinical presentation, specific clinical exam findings can assist in treating and preventing CVD. CVD may initially manifest as pulmonary pathology, and thus, accurate cardiopulmonary auscultation is paramount to establishing accurate diagnosis. One of the most powerful tools available to physicians is the stethoscope. The stethoscope first emerged in the year 1818, invented by a French physician, René Laennec. Since then, the initial modest monaural wooden tube has evolved into a sophisticated digital device. This paper provides an analysis of the evolution of the stethoscope as well as highlights the advancement made by the modern digital stethoscope including the application of this tool in advancing care for patients suffering from CVD.