BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals.

BACKGROUND AND OBJECTIVE: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, no publicly available databases with both respiratory sound and EIT data are available. METHODS: In this work, we have assembled the first open-access bimodal database focusing on the differential diagnosis of respiratory diseases (BRACETS: Bimodal Repository of Auscultation Coupled with Electrical Impedance Thoracic Signals). It includes simultaneous recordings of single and multi-channel respiratory sounds and EIT. Furthermore, we have proposed several machine learning-based baseline systems for automatically classifying respiratory diseases in six distinct evaluation tasks using respiratory sound and EIT (A1, A2, A3, B1, B2, B3). These tasks included classifying respiratory diseases at sample and subject levels. The performance of the classification models was evaluated using a 5-fold cross-validation scheme (with subject isolation between folds). RESULTS: The resulting database consists of 1097 respiratory sounds and 795 EIT recordings acquired from 78 adult subjects in two countries (Portugal and Greece). In the task of automatically classifying respiratory diseases, the baseline classification models have achieved the following average balanced accuracy: Task A1 - 77.9±13.1%; Task A2 - 51.6±9.7%; Task A3 - 38.6±13.1%; Task B1 - 90.0±22.4%; Task B2 - 61.4±11.8%; Task B3 - 50.8±10.6%. CONCLUSION: The creation of this database and its public release will aid the research community in developing automated methodologies to assess and monitor respiratory function, and it might serve as a benchmark in the field of digital medicine for managing respiratory diseases. Moreover, it could pave the way for creating multi-modal robust approaches for that same purpose.

Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders.

Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

Hemopneumothorax detection through the process of artificial evolution - a feasibility study.

BACKGROUND: Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. METHODS: Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model. Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air or saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. RESULTS: The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy, sensitivity and specificity ranging from 64 to 100%, 63 to 100%, and 63 to 100%, respectively, in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more, and regardless of background noise. CONCLUSIONS: We present a novel, objective device for rapid diagnosis of potentially lethal thoracic injuries. With further optimization, such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

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

ABSTRACT: 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 ±â€Š0.090) were significantly higher than those in the lung fields without ILDs (0.032 ±â€Š0.023, 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.

A basic investigation into the optimization of cylindrical tubes used as acoustic stethoscopes for auscultation in COVID-19 diagnosis.

During the COVID-19 outbreak, the auscultation of heart and lung sounds has played an important role in the comprehensive diagnosis and real-time monitoring of confirmed cases. With clinicians wearing protective clothing in isolation wards, a potato chip tube stethoscope, which is a secure and flexible substitute for a conventional stethoscope, has been used by Chinese medical workers in the first-line treatment of COVID-19. In this study, an optimal design for this simple cylindrical stethoscope is proposed based on the fundamental theory of acoustic waveguides. Analyses of the cutoff frequency, sound power transmission coefficient, and sound wave propagation in the uniform lossless tube provide theoretical guidance for selecting the geometric parameters for this simple cylindrical stethoscope. A basic investigation into the auscultatory performances of the original tube and the optimal tube with proposed dimensions was conducted both in a semi-anechoic chamber and in a quiet laboratory. Both experimental results and front-line doctors' clinical feedback endorse the proposed theoretical optimization.

Evidence of the Sequential Changes of Lung Sounds in COVID-19 Pneumonia Using a Novel Wireless Stethoscope with the Telemedicine System.

A 60-year-old woman was admitted to our hospital due to coronavirus disease 2019 (COVID-19) pneumonia with a chief complaint of persistent low-grade fever and dry cough for two weeks. Thoracic computed tomography demonstrated a crazy paving pattern in the bilateral lower lobes. In a COVID-19 ward, we used a novel wireless stethoscope with a telemedicine system and successfully recorded and shared the lung sounds in real-time between the red and green zones. The fine crackles at the posterior right lower lung fields changed from mid-to-late (day 1) to late inspiratory crackles (day 3), which disappeared at day 5 along with an improvement in both the clinical symptoms and thoracic CT findings.

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes.

BACKGROUND: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose. METHODS: One hundred and ninety two auscultation recordings collected from children using two different digital stethoscopes (Clinicloud™ and Littman™) were each tagged as containing wheezes, crackles or neither by a pediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis, with a subset validated by a blinded second clinician. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds. RESULTS: With optimized AI detection thresholds, crackle detection positive percent agreement (PPA) was 0.95 and negative percent agreement (NPA) was 0.99 for Clinicloud recordings; for Littman-collected sounds PPA was 0.82 and NPA was 0.96. Wheeze detection PPA and NPA were 0.90 and 0.97 respectively (Clinicloud auscultation), with PPA 0.80 and NPA 0.95 for Littman recordings. CONCLUSIONS: AI can detect crackles and wheeze with a reasonably high degree of accuracy from breath sounds obtained from different digital stethoscope devices, although some device-dependent differences do exist.

A Wearable Stethoscope for Long-Term Ambulatory Respiratory Health Monitoring.

Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance.

René Théophile Hyacinthe Laënnec (1781-1826). Two hundred years of the stethoscope. A brief overview. / René Théophile Hyacinthe Laënnec (1781-1826). Doscientos años del uso del estetoscopio. Una breve reseña.

Two hundred years have passed since the publication that revealed the clinical use of the stethoscope. René Théophile Hyacinthe Laënnec published it in 1819. Laënnec spent his childhood in the social effervescence of the French Revolution and studied medicine in Paris, where he graduated in 1804. His clinical experience at Necker Hospital peaked with the invention of the stethoscope in 1816. Three years later, he published his masterpiece De L'Auscultation Médiate, which underlined a more rational clinical-pathological approach, especially in the understanding of cardiopulmonary diseases. Undoubtedly, Laënnec revolutionized medicine by perfecting the art of thoracic semiology, which allowed him to translate the sounds he heard into an image that could be visualized. In the bicentennial of the invention of such fundamental milestone in modern medicine, the purpose of this article is to go over its history.

Se han cumplido doscientos años desde la publicación en la que se dio a conocer la aplicación clínica del estetoscopio. Esta fue realizada en 1819 por René Théophile Hyacinthe Laënnec. El Dr. Laënnec vivió su infancia en la efervescencia social de la Revolución francesa y estudió Medicina en París, donde se graduó en 1804. Su experiencia clínica en el Hospital Necker culminó con la invención del estetoscopio en 1816. Tres años después, la publicación de su obra maestra De l'auscultation médiate enfatizó un enfoque clínico-patológico más racional, en especial, para el entendimiento de las enfermedades cardiorrespiratorias. Sin duda, el Dr. Laënnec revolucionó la medicina al perfeccionar el arte de la semiología torácica, que permitió al médico transformar los sonidos que escuchaba en una imagen, la cual podía visualizar. Con ocasión del bicentenario de este trascendental hito de la medicina moderna, se recuerda su historia.

Influence of Age on Upper Arm Cuff Blood Pressure Measurement.

Blood pressure (BP) is a leading global risk factor. Increasing age is related to changes in cardiovascular physiology that could influence cuff BP measurement, but this has never been examined systematically and was the aim of this study. Cuff BP was compared with invasive aortic BP across decades of age (from 40 to 89 years) using individual-level data from 31 studies (1674 patients undergoing coronary angiography) and 22 different cuff BP devices (19 oscillometric, 1 automated auscultation, 2 mercury sphygmomanometry) from the Invasive Blood Pressure Consortium. Subjects were aged 64±11 years, and 32% female. Cuff systolic BP overestimated invasive aortic systolic BP in those aged 40 to 49 years, but with each older decade of age, there was a progressive shift toward increasing underestimation of aortic systolic BP (P<0.0001). Conversely, cuff diastolic BP overestimated invasive aortic diastolic BP, and this progressively increased with increasing age (P<0.0001). Thus, there was a progressive increase in cuff pulse pressure underestimation of invasive aortic PP with increasing decades of age (P<0.0001). These age-related trends were observed across all categories of BP control. We conclude that cuff BP as an estimate of aortic BP was substantially influenced by increasing age, thus potentially exposing older people to greater chance for misdiagnosis of the true risk related to BP.

Assessment of breath sounds at birth using digital stethoscope technology.

Newborn transition is a phase of complex change involving lung fluid clearance and lung aeration. We aimed to use a digital stethoscope (DS) to assess the change in breath sound characteristics over the first 2 h of life and its relationship to mode of delivery. A commercially available DS was used to record breath sounds of term newborns at 1-min and 2-h post-delivery via normal vaginal delivery (NVD) or elective caesarean section (CS). Sound analysis was conducted, and two comparisons were carried out: change in frequency profiles over 2 h, and effect of delivery mode. There was a significant drop in the frequency profile of breath sounds from 1 min to 2 h with mean (SD) frequency decreasing from 333.74 (35.42) to 302.71 (47.19) Hz, p < 0.001, and proportion of power (SD) in the lowest frequency band increasing from 0.27 (0.11) to 0.37 (0.15), p < 0.001. At 1 min, NVD infants had slightly higher frequency than CS but no difference at 2 h.Conclusion: We were able to use DS technology in the transitioning infant to depict significant changes to breath sound characteristics over the first 2 h of life, reflecting the process of lung aeration.What is Known:• Lung fluid clearance and lung aeration are critical processes that facilitate respiration and mode of delivery can impact this• Digital stethoscopes offer enhanced auscultation and have been used in the paediatric population for the assessment of pulmonary and cardiac soundsWhat is New:• This is the first study to use digital stethoscope technology to assess breath sounds at birth• We describe a change in breath sound characteristics over the first 2 h of life and suggest a predictive utility of this analysis to predict the development of respiratory distress in newborns prior to the onset of symptoms.

Blood pressure measurement by oscillometric and auscultatory methods in normotensive pregnant women.

OBJECTIVE: to compare blood pressure values obtained by auscultatory and oscillometric methods in different gestational periods, considering cuff width. METHOD: it is a cross-sectional and quasi-experimental study approved by the Research Ethics Committee. The sample consisted of 108 low-risk pregnant women. Blood pressure measurements were performed in gestational periods of 10-14, 19-22 and 27-30 weeks. RESULTS: The oscillometric device presented values similar to the auscultatory method in systolic blood pressure, but overestimated diastolic blood pressure. Underestimation of blood pressure occurred when using the standard width cuff rather than the correct width cuff in both measuring methods. CONCLUSION: Verification of brachial circumference and use of adequate cuffs in both methods are indispensable to obtain reliable blood pressure values in pregnant women. We recommend performance of additional studies to evaluate diastolic blood pressure overestimation by the Microlife 3BTO-A.

Auscultación asimétrica: lo que la verdad esconde / Asymmetric auscultation: What the truth hides

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The Association of High Resolution Cervical Auscultation Signal Features With Hyoid Bone Displacement During Swallowing.

Recent publications have suggested that high-resolution cervical auscultation (HRCA) signals may provide an alternative non-invasive option for swallowing assessment. However, the relationship between hyoid bone displacement, a key component to safe swallowing, and HRCA signals is not thoroughly understood. Therefore, in this work we investigated the hypothesis that a strong relationship exists between hyoid displacement and HRCA signals. Videofuoroscopy data was collected for 129 swallows, simultaneously with vibratory/acoustic signals. Horizontal, vertical and hypotenuse displacements of the hyoid bone were measured through manual expert analysis of videofluoroscopy images. Our results showed that the vertical displacement of both the anterior and posterior landmarks of the hyoid bone was strongly associated with the Lempel-Ziv complexity of superior-inferior and anterior-posterior vibrations from HRCA signals. Horizontal and hypotenuse displacements of the posterior aspect of the hyoid bone were strongly associated with the standard deviation of swallowing sounds. Medial-Lateral vibrations and patient characteristics such as age, sex, and history of stroke were not significantly associated with the hyoid bone displacement. The results imply that some vibratory/acoustic features extracted from HRCA recordings can provide information about the magnitude and direction of hyoid bone displacement. These results provide additional support for using HRCA as a non-invasive tool to assess physiological aspects of swallowing such as the hyoid bone displacement.

Intermittent auscultation fetal monitoring during labour: A systematic scoping review to identify methods, effects, and accuracy.

BACKGROUND: Intermittent auscultation (IA) is the technique of listening to and counting the fetal heart rate (FHR) for short periods during active labour and continuous cardiotocography (CTC) implies FHR monitoring for longer periods. Although the evidence suggests that IA is the best way to monitor healthy women at low risk of complications, there is no scientific evidence for the ideal device, timing, frequency and duration for IA. We aimed to give an overview of the field, identify and describe methods and practices for performing IA, map the evidence and accuracy for different methods of IA, and identify research gaps. METHODS: We conducted a systematic scoping review following the Joanna Briggs methodology. Medline, EMBASE, Cinahl, Maternity & Infant Care, Cochrane Library, SveMed+, Web of Science, Scopus, Lilacs and African Journals Online were searched for publications up to January 2019. We did hand searches in relevant articles and databases. Studies from all countries, international guidelines and national guidelines from Denmark, United Kingdom, United States, New Zealand, Australia, The Netherlands, Sweden, Denmark, and Norway were included. We did quality assessment of the guidelines according to the AGREEMENT tool. We performed a meta-analysis assessing the effects of IA with a Doppler device vs. Pinard device using methods described in The Cochrane Handbook, and we performed an overall assessment of the summary of evidence using the GRADE approach. RESULTS: The searches generated 6408 hits of which 26 studies and 11 guidelines were included in the review. The studies described slightly different techniques for performing IA, and some did not provide detailed descriptions. Few of the studies provided details of normal and abnormal IA findings. All 11 guidelines recommended IA for low risk women, although they had slightly different recommendations on the frequency, timing, and duration for IA, and the FHR characteristics that should be observed. Four of the included studies, comprising 8436 women and their babies, were randomised controlled trials that evaluated the effect of IA with a Doppler device vs. a Pinard device. Abnormal FHRs were detected more often using the Doppler device than in those using the Pinard device (risk ratio 1.77; 95% confidence interval 1.29-2.43). There were no significant differences in any of the other maternal or neonatal outcomes. Four studies assessed the accuracy of IA findings. Normal FHR was easiest to identify correctly, whereas identifying periodic FHR patterns such as decelerations and saltatory patterns were more difficult. CONCLUSION: Although IA is the recommended method, no trials have been published that evaluate protocols on how to perform it. Nor has any study assessed interrater agreements regarding interpretations of IA findings, and few have assessed to what degree clinicians can describe FHR patterns detected by IA. We found no evidence to recommend Doppler device instead of the Pinard for IA, or vice versa.

Diagnostic accuracy of a velcro sound detector (VECTOR) for interstitial lung disease in rheumatoid arthritis patients: the InSPIRAtE validation study (INterStitial pneumonia in rheumatoid ArThritis with an electronic device).

BACKGROUND: Interstitial lung disease (ILD) is a severe systemic manifestation of rheumatoid arthritis (RA). High-resolution computed tomography (HRCT) represents the gold standard for the diagnosis of ILD, but its routine use for screening programs is not advisable because of both high cost and X-ray exposure. Velcro crackles at lung auscultation occur very early in the course of interstitial pneumonia, and their detection is an indication for HRCT. Recently, we developed an algorithm (VECTOR) to detect the presence of Velcro crackles in pulmonary sounds and showed good results in a small sample of RA patients. The aim of the present investigation was to validate the diagnostic accuracy of VECTOR in a larger population of RA patients, compared with that of the reference standard of HRCT, from a multicentre study. METHODS: To avoid X-ray exposure, we enrolled 137 consecutive RA patients who had recently undergone HRCT. Lung sounds of all patients were recorded in 4 pulmonary fields bilaterally with a commercial electronic stethoscope (ES); subsequently, all HRCT images were blindly evaluated by a radiologist, and audio data were analysed by means of VECTOR. RESULTS: Fifty-nine of 137 patients showed ILD (43.1%). VECTOR correctly classified 115/137 patients, showing a diagnostic accuracy of 83.9% and a sensitivity and specificity of 93.2 and 76.9%, respectively. CONCLUSIONS: VECTOR may represent the first validated tool for the screening of RA patients who are suspected for ILD and who should be directed to HRCT for the diagnosis. Moreover, early identification of RA-ILD could contribute to the design of prospective studies aimed at elucidating unclear aspects of the disease.

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles. In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. The gathered data show that machine learning (ML)-based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score.Conclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds. What is Known: • Auscultation performance of average physician is very low. AI solutions presented in scientific literature are based on small data bases with isolated pathological sounds (which are far from real recordings) and mainly on leave-one-out validation method thus they are not reliable. What is New: • AI learning process was based on thousands of signals from real patients and a reliable description of recordings was based on multiple validation by physicians and acoustician resulting in practical and statistical prove of AI high performance.

Investigation of Stethoscope Technology for En Route Care.

OBJECTIVE: Occupational challenges in air transport domains make auscultation with traditional stethoscopes difficult. This study aimed to investigate two commercial off-the-shelf stethoscopes for use in high noise military patient transport environments. The stethoscopes were assessed by Aeromedical Evacuation providers in a simulated C-130 trainer on live standardized mock patients. Device 1 was a dual-mode stethoscope developed for rotary wing military airframes. Device 2 was an electronic stethoscope developed for high noise civilian environments. Twenty clinicians performed cardiopulmonary auscultation using the devices on the same two standardized patients in a simulated C-130 then completed a subjective questionnaire on their ability to identify heart and lung sounds. Results indicated the dual-mode stethoscope had limited utility with clinician likeliness of use rated as low (median = 2; interquartile range = 1.75-3.25), whereas the electronic stethoscope had potential utility with likeliness of use rated as good (median = 4; interquartile range = 3.25-5). We conclude that further examination of devices capable of auscultation in high noise military environments is needed. In-flight testing of device 2 for use by end users has been completed and will be reported in a separate manuscript.