Auscultation of the respiratory system

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.

Wearing surgical face mask has no significant impact on auscultation assessment

Objective

During the COVID-19 pandemic, universal mask-wearing became one of the main public health interventions. Because of this, most physical examinations, including lung auscultation, were done while patients were wearing surgical face masks. The aim of this study was to investigate whether mask wearing has an impact on pulmonologist assessment during auscultation of the lungs.

Methods

This was a repeated measures crossover design study. Three pulmonologists were instructed to auscultate patients with previously verified prolonged expiration, wheezing, or crackles while patients were wearing or not wearing masks (physician and patients were separated by an opaque barrier). As a measure of pulmonologists' agreement in the assessment of lung sounds, we used Fleiss kappa (K).

Results

There was no significant difference in agreement on physician assessment of lung sounds in all three categories (normal lung sound, duration of expiration, and adventitious lung sound) whether the patient was wearing a mask or not, but there were significant differences among pulmonologists when it came to agreement of lung sound assessment.

Conclusion

Clinicians and health professionals are safer from respiratory infections when they are wearing masks, and patients should be encouraged to wear masks because our research proved no significant difference in agreement on pulmonologists' assessment of auscultated lung sounds whether or not patients wore masks.

Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification

Goal: In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has become increasingly crucial. Traditional diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), while accurate, often face accessibility challenges. Lung auscultation, a simpler alternative, is subjective and highly dependent on the clinician's expertise. The pandemic has further exacerbated these challenges by restricting face-to-face consultations. This study aims to overcome these limitations by developing an automated respiratory sound classification system using deep learning, facilitating remote and accurate diagnoses. Methods: We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. Our model is enhanced with attention feature fusion of low-to-high-level information based on a knowledge propagation mechanism to increase classification effectiveness. This novel approach was evaluated using the ICBHI benchmark dataset and a larger, self-collected Pediatric dataset comprising outpatient children aged 1 to 6 years. Results: The proposed CNN model with knowledge propagation demonstrated superior performance compared to existing state-of-the-art models. Specifically, our model showed higher sensitivity in detecting abnormalities in the Pediatric dataset, indicating its potential for improving the accuracy of respiratory disease diagnosis. Conclusions: The integration of a knowledge propagation mechanism into a CNN model marks a significant advancement in the field of automated diagnosis of respiratory disease. This study paves the way for more accessible and precise healthcare solutions, which is especially crucial in pandemic scenarios.

Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study

BACKGROUND

Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers.

METHODS

Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level.

RESULTS

222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording.

CONCLUSIONS

The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study

Background

The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians.

Objective

This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users.

Methods

Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree").

Results

Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5).

Conclusions

Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.

Vocal resonance: a narrative review

Physical examination is an important ritual of bedside medicine that establishes a strong bond between the patient and the physician. It provides practice to acquire important diagnostic skills. A poorly executed bedside examination may result in the wrong diagnosis and adverse outcomes. However, the ritual of obtaining a patient's history and performing a good clinical examination is declining globally. Even the quality of clinical examination skills is declining. One reason may be the short time spent by physicians at the bedside of patients. In addition, due to the substantial technological advancement, physicians often rely more on technology and consider clinical examinations less relevant. In resource-limited settings, thorough history-taking and physical examinations should always be prioritized. An important aspect of respiratory auscultation is the auscultation over the chest wall to detect abnormalities in the transmission of voice-generated sounds, which may provide an important diagnostic clue. Laënnec originally described in detail three types of voice-generated sounds and named them bronchophonism, pectoriloquism, and egophonism. Subsequently, they are known as bronchophony, whispering pectoriloquy, and egophony. A recent variant of egophony is "E-to-A" changes. We searched PubMed, EMBASE, and the CINAHL from inception to December 2023. We used the following search terms: vocal resonance, bronchophony, egophony, whispering pectoriloquy, auscultation, etc. All types of studies were chosen. This review will narrate the physics of sound waves, the types of vocal resonance, the mechanisms of vocal resonance, the methods to elicit them, and the accuracy of vocal resonance.

Lung ultrasound in the evaluation of pulmonary edema in newborns with critical congenital heart disease

BACKGROUND

Newborns with critical congenital heart disease (CCHD) with increased pulmonary blood flow (PBF) are at high risk for congestive heart failure. In this study, we aimed to evaluate the presence and degree of pulmonary edema in newborns with CCHD using lung ultrasound (LUS) during the perioperative period.

METHODS

Prospective clinical trial, 44 newborn patients with CCHD were evaluated in this prospective clinical trial. LUS was repeatedly performed to determine the course of pulmonary edema during the perioperative period. LUS was performed simultaneously with chest radiography (CXR), which was the main part of patient management. The primary outcome of this study was to identify whether a correlation existed between LUS and CXR findings. The secondary outcomes were to determine the relationship between LUS and the need for respiratory support, diuretic use, vasoactive inotropic score (VIS), and pro-B-type natriuretic peptide (pro-BNP) levels during the perioperative period.

RESULTS

The mean gestational age of the patients was 38.3 ± 1.7 weeks, with a mean birth weight of 3026 ± 432 g. In the preoperative period, both LUS and CXR images were consistent with clinical signs of pulmonary edema. On the first postoperative day, pulmonary edema increased compared to the preoperative period but gradually decreased by the 6th day of surgery (p < 0.05). Positive correlations were observed between the LUS and CXR findings at all study points (p < 0.05). The LUS findings exhibited trends parallel to those of VIS, serum pro-BNP levels, need for respiratory support, and diuretic requirements. As expected, these trends were more pronounced in CCHDs where PBF increased.

CONCLUSION

In CCHD, serial lung ultrasound (LUS) assessments, particularly in cases with increased PBF, can provide valuable guidance for managing patients during the perioperative period.

Anatomical Ignorance Resulting in Iatrogenic Causes of Human Morbidity

This article discusses how inadequate anatomy education contributes to iatrogenic causes of human morbidity and mortality. Through a review of the relevant literature, high-yield clinical cases were identified in which a lack of sufficient anatomical knowledge contributed to patient morbidity, such as abscess formation and neuropathy as a result of improper intramuscular injections, superior gluteal nerve injuries due to surgical procedures, and misdiagnoses due to physicians' inability to examine and correlate clinical and radiological findings. The importance of a multimodal learning approach in anatomy education for medical students, which includes the utilization of the cadaveric dissection approach to emphasize spatial understanding, is crucial for the development of competent physicians with a deep-rooted foundational knowledge of anatomy and related concepts, such as physiology, pathology, and radiology. It cannot be understated that anatomy education and a lack of knowledge of anatomy and related concepts may influence iatrogenic causes of human morbidity and mortality. Therefore, all efforts should be made to ensure that students develop a strong foundational anatomy knowledge during their preclinical years.

Enhancing respiratory physiology education: innovative wet spirometer modifications for hands-on learning

The use of wet spirometers, although once common, has greatly declined because these devices measure only static lung volumes and students often face technical issues in their use. In this study, the wet spirometer has been modified to investigate the fundamental laws of flow and different types of lung disease. This modification was achieved by changing the dimensions of the device, printing a scale on the bell, and attaching an airflow control system (ACS) between the corrugated tube and hollow tube of the inner cylinder. The ACS allowed for flow control during the exercises. Two exercises were performed: exercise I compared the parameters measured by the wet spirometer, modified spirometer, and computerized spirometer to determine the suitability of the modification, while exercise II tested the variables affecting flow. These exercises introduce students to data collection, analysis, and the use of statistical tests as they compare various spirometers. Additionally, students gain valuable experience in experimental design by conducting diverse experiments that investigate factors influencing flow dynamics. By plotting the results and participating in small group discussions, students can apply flow principles in respiratory and circulatory systems, offering a hands-on experience that integrates physics and physiology. The modified spirometer facilitated multifaceted topic exploration, surpassing the traditional wet spirometer's capabilities.NEW & NOTEWORTHY This activity involves cost-effective modifications to the wet spirometer, broadening its applicability. These modifications effectively address student challenges associated with wet spirometer handling and enhance comprehension of fluid dynamics, all without the need for costly simulations, wet experiments, or fragile instruments. By offering a hands-on experience without traditional limitations, our modified spirometer provides an accessible and engaging approach to respiratory physiology education.

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Clinical Review of Hypertensive Acute Heart Failure

Although acute heart failure (AHF) is a common disease associated with significant symptoms, morbidity and mortality, the diagnosis, risk stratification and treatment of patients with hypertensive acute heart failure (H-AHF) still remain a challenge in modern medicine. Despite great progress in diagnostic and therapeutic modalities, this disease is still accompanied by a high rate of both in-hospital (from 3.8% to 11%) and one-year (from 20% to 36%) mortality. Considering the high rate of rehospitalization (22% to 30% in the first three months), the treatment of this disease represents a major financial blow to the health system of each country. This disease is characterized by heterogeneity in precipitating factors, clinical presentation, therapeutic modalities and prognosis. Since heart decompensation usually occurs quickly (within a few hours) in patients with H-AHF, establishing a rapid diagnosis is of vital importance. In addition to establishing the diagnosis of heart failure itself, it is necessary to see the underlying cause that led to it, especially if it is de novo heart failure. Given that hypertension is a precipitating factor of AHF and in up to 11% of AHF patients, strict control of arterial blood pressure is necessary until target values are reached in order to prevent the occurrence of H-AHF, which is still accompanied by a high rate of both early and long-term mortality.

Breath characteristics and adventitious lung sounds in healthy and asthmatic horses

BACKGROUND

Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists.

OBJECTIVES

First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses.

ANIMALS

Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA-], and 5 in exacerbation [sEA+]).

METHODS

Prospective multicenter case-control study. Horses were categorized based on the horse owner-assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One-hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed.

RESULTS

Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P < .001, P < .01, P = .01, P < .01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA- (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, -9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23-point weighted clinical score (WCS23).

CONCLUSIONS AND CLINICAL IMPORTANCE

The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA- horses.

Exploring classical machine learning for identification of pathological lung auscultations

The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despite improvements in digital stethoscopes and attempts to find synergy between them and artificial intelligence, solutions for their use in clinical settings remain scarce. Physicians continue to infer initial diagnoses with less sophisticated means, resulting in low accuracy, leading to suboptimal patient care. To arrive at a correct preliminary diagnosis, the auscultation diagnostics need to be of high accuracy. Due to the large number of auscultations performed, data availability opens up opportunities for more effective sound analysis. In this study, digital 6-channel auscultations of 45 patients were used in various machine learning scenarios, with the aim of distinguishing between normal and abnormal pulmonary sounds. Audio features (such as fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive statistics of log energy, RMS and MFCC) were extracted using the Python library Surfboard. Windowing, feature aggregation, and concatenation strategies were used to prepare data for machine learning algorithms in unsupervised (fair-cut forest, outlier forest) and supervised (random forest, regularized logistic regression) settings. The evaluation was carried out using 9-fold stratified cross-validation repeated 30 times. Decision fusion by averaging the outputs for a subject was also tested and found to be helpful. Supervised models showed a consistent advantage over unsupervised ones, with random forest achieving a mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score 0.675) in side-based detection and a mean AUC ROC of 0.721 (accuracy 68.89%, Kappa 0.371, F1-score 0.650) in patient-based detection.

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

Exploring Microphone Technologies for Digital Auscultation Devices

The aim of this work is to present a preliminary study for the design of a digital auscultation system, i.e., a novel wearable device for patient chest auscultation and a digital stethoscope. The development and testing of the electronic stethoscope prototype is reported with an emphasis on the description and selection of sound transduction systems and analog electronic processing. The focus on various microphone technologies, such as micro-electro-mechanical systems (MEMSs), electret condensers, and piezoelectronic diaphragms, intends to emphasize the most suitable transducer for auscultation. In addition, we report on the design and development of a digital acquisition system for the human body for sound recording by using a modular device approach in order to fit the chosen analog and digital mics. Tests were performed on a designed phantom setup, and a qualitative comparison between the sounds recorded with the newly developed acquisition device and those recorded with two commercial digital stethoscopes is reported.

A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring

Early prevention and detection of respiratory disease have attracted extensive attention due to the significant increase in people with respiratory issues. Restraining the spread and relieving the symptom of this disease is essential. However, the traditional auscultation technique demands a high-level medical skill, and computational respiratory sound analysis approaches have limits in constrained locations. A wearable auscultation device is required to real-time monitor respiratory system health and provides consumers with ease. In this work, we developed a Respiratory Sound Diagnosis Processor Unit (RSDPU) based on Long Short-Term Memory (LSTM). The experiments and analyses were conducted on feature extraction and abnormality diagnosis algorithm of respiratory sound, and Dynamic Normalization Mapping (DNM) was proposed to better utilize quantization bits and lessen overfitting. Furthermore, we developed the hardware implementation of RSDPU including a corrector to filter diagnosis noise. We presented the FPGA prototyping verification and layout of the RSDPU for power and area evaluation. Experimental results demonstrated that RSDPU achieved an abnormality diagnosis accuracy of 81.4 %, an area of 1.57 × 1.76 mm under the SMIC 130 nm process, and power consumption of 381.8 μW, which met the requirements of high accuracy, low power consumption, and small area.

Effect of wheeze and lung function on lung sound parameters in children with asthma

BACKGROUND

In children with asthma, there are many cases in which wheeze is confirmed by auscultation with a normal lung function, or in which the lung function is decreased without wheeze. Using an objective lung sound analysis, we examined the effect of wheeze and the lung function on lung sound parameters in children with asthma.

METHODS

A total of 114 children with asthma (males to females = 80: 34, median age 10 years old) were analyzed for their lung sound parameters using conventional methods, and wheeze and the lung function were checked. The effects of wheeze and the lung function on lung sound parameters were examined.

RESULTS

The patients with wheeze or decreased forced expiratory flow and volume in 1 s (FEV1) (% pred) showed a significantly higher sound power of respiration and expiration-to-inspiration sound power ratio (E/I) than those without wheeze and a normal FEV1 (% pred). There was no marked difference in the sound power of respiration or E/I between the patients without wheeze and a decreased FEV1 (% pred) and the patients with wheeze and a normal FEV1 (% pred).

CONCLUSIONS

Our data suggest that bronchial constriction in the asthmatic children with wheeze similarly exists in the asthmatic children with a decreased lung function. A lung sound analysis is likely to enable an accurate understanding of airway conditions.

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.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

Lung anomaly detection from respiratory sound database (sound signals)

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Interpretation of lung disease classification with light attention connected module

Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.

Camera-based Respiratory Imaging for Thoracic Asymmetry in Thoracic Surgery Patients

The current tool for assessing thoracic asymmetry of thoracic surgery patients is inappropriate for timely or frequent clinical routines due to its dependency on empirical physical examinations or specialized machines. This study investigates the camera-based respiratory imaging for screening thoracic asymmetry, in an intelligent and convenient way. The respiratory heatmaps are generated based on the respiratory magnitudes, phases and angles extracted from the chest video, and bilateral chest region of interest are compared statistically. Due to the variability of chest respiratory direction, spatial enhancement (SDR and SPCA) algorithms are proposed to magnify the respiratory energy. The proposed framework was validated in a clinical trial involving 31 patients, recorded by a smartphone camera. A high correlation was found between the camera measurements and patients' thoracic status in both the visual imaging and quantified indices. The respiratory imaging of camera shows a clear potential for assessing chest abnormalities of thoracic surgery patients.

Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

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

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.

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.

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] ± 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 ± 0.10), wheezing disorders (AUROC 0.91 ± 0.03), and bronchiolitis (AUROC 0.94 ± 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: A Comparative Study

BACKGROUND

To date, performance comparisons between men and machines have been performed in many health domains. Yet, machine learning models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored.

OBJECTIVE

The primary objective of this study is to compare human clinicians and a machine learning model in predicting COVID-19 from respiratory sound recordings.

METHODS

In this study, we compare human clinicians and a machine learning model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses is compared with predictions made by a machine learning model trained on 1,162 samples. Each sample consists of voice, cough, and breathing sound recordings from one subject, and the length of each sample is around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance, in terms of both accuracy and confidence.

RESULTS

The machine learning model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, while the best performance achieved by the clinician was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating clinicians' and model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92.

CONCLUSIONS

Our findings suggest that the clinicians and the machine learning model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

Preoperative lung ultrasound for confirming the double-lumen endotracheal tube position for one-lung ventilation: A systematic review and meta-analysis

Objectives

Insertion of a double-lumen endotracheal tube (DLT) is the most commonly used method for one-lung ventilation (OLV). This meta-analysis was aimed at investigating the performance of lung ultrasound in assessing the DLT position in OLV.

Methods

Electronic databases were searched for related trials from inception to October 2022. The primary outcome was the performance of ultrasound or clinical evaluation in confirming the correctness of the DLT position, using fiberoptic bronchoscopy or intraoperative direct visualization of lung collapse as the gold standard. The secondary outcome was the time required to confirm or adjust the DTL position.

Results

Five randomized controlled trials and three observational studies involving 771 patients were included in the meta-analysis. The pooled sensitivity and specificity of ultrasound were 0.93 (95% confidence interval [CI]: 0.79-0.98) and 0.61 (95% CI: 0.41-0.77), respectively, while those of clinical evaluation were 0.93 (95% CI: 0.73-0.99) and 0.35 (95% CI: 0.25-0.47), respectively. The pooled procedure duration was 122.27 s (95% CI: 20.85-223.69) with ultrasound and 112.03 s (95% CI: 95.30-128.76) with clinical evaluation. The area under the curve for discriminating the DLT position was 0.86 (95% CI: 0.82-0.88) for ultrasound and 0.52 (95% CI: 0.48-0.57) for clinical evaluation.

Conclusions

Compared to clinical evaluation, ultrasound has a similar sensitivity but a better specificity for confirming the correctness of the DLT position. Ultrasound is an acceptable imaging tool for assessing DTL placement in OLV.

Headache, Fever, and Myalgias in an HIV-Positive Male with a History of Tuberculosis: Epstein–Barr Virus Aseptic Meningitis

Background: We describe a case of EBV aseptic meningitis in a patient with HIV with an extensive history of prior infections and exposures. Detailed Case Description: A 35-year-old man with a history of HIV, syphilis, and partially treated tuberculosis presented with headache, fever, and myalgias. He reported recent exposure to dust from a construction site and had sexual contact with a partner with active genital lesions. An initial workup revealed mildly elevated inflammatory markers, significant pulmonary scarring from tuberculosis with a classic “weeping willow sign”, and lumbar puncture findings consistent with aseptic meningitis. An extensive evaluation was conducted to identify causes of bacterial and viral meningitis, including syphilis. Immune reconstitution inflammatory syndrome and isoniazid-induced aseptic meningitis were also considered based on his medications. EBV was ultimately isolated through PCR from the patient’s peripheral blood. The patient’s condition improved, and he was discharged on his home antiretroviral and anti-tuberculous treatment. Conclusion: Central nervous system infections represent unique challenges in patients with HIV. EBV reactivation can present with atypical symptoms and should be considered as a cause of aseptic meningitis in this population.

Acute chest syndrome in adult patients with sickle cell disease: The relationship with the time to onset after hospital admission

Data on acute chest syndrome (ACS) in adult sickle cell disease patients are scarce. In this study, we describe 105 consecutive ACS episodes in 81 adult patients during a 32-month period and compare the characteristics as a function of the time to onset after hospital admission for a vaso-occlusive crisis (VOC), that is early-onset episodes (time to onset ≤24 h, 42%) versus secondary episodes (>24 h, 58%; median [interquartile range] time to onset: 2 [2-3] days). The median age was 27 [22-34] years, 89% of the patients had an S/S or S/β⁰ -thalassaemia genotype; 81% of the patients had a history of ACS (median: 3 [2-5] per patient), only 61% were taking a disease-modifying treatment at the time of the ACS. Fever and chest pain were noted in respectively 54% and 73% of the episodes. Crackles (64%) and bronchial breathing (32%) were the main abnormal auscultatory findings. A positive microbiological test was found for 20% of episodes. Fifty percent of the episodes required a blood transfusion; ICU transfer and mortality rates were respectively 29% and 1%. Secondary and early-onset forms of ACS did not differ significantly. Disease-modifying treatments should be revaluated after each ACS episode because the recurrence rate is high.

A Rare Case of Pulmonary Cavitary Disease Caused by Mycobacterium xenopi

Mycobacterium xenopi is a slow-growing, acid-fast, non-tuberculous mycobacterium (NTM). It is often considered to be a saprophyte or an environmental contaminant. Mycobacterium xenopi has low pathogenicity and is usually seen in patients with pre-existing chronic lung diseases and immunocompromised patients. We present a case of Mycobacterium xenopi causing a cavitary lesion in a patient with chronic obstructive pulmonary disease (COPD) that was discovered incidentally during the low-dose CT scan done for lung cancer screening in a patient with COPD. The initial workup was negative for NTM. An Interventional-guided (IR) core needle biopsy was done given the high suspicion for NTM and revealed a positive culture for Mycobacterium xenopi.  Our case highlights the importance of considering NTM in the differential diagnosis of at-risk patients and pursuing invasive testing if there is a high clinical suspicion.

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) "Healthy" or "COPD" and (2) "Healthy", "COPD", or "Pneumonia" classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

The Buddhasothorn Asthma Severity Score (BASS): A practical screening tool for predicting severe asthma exacerbations for pediatric patients

BACKGROUND AND AIM

A precise scaling system of acute asthma leads to an accurate assessment of disease severity. This study aimed to compare the accuracy of the Buddhasothorn Asthma Severity Score (BASS) with the Wood-Downes-Ferrés Scale (WDFS) to recognize the severity level of acute asthma.

MATERIALS AND METHODS

A cross-sectional study was conducted comprising Thai children aged 2-15 years with acute asthma. The BASS and WFDS were rated once in the emergency department. The degree of severity was determined by frequency and type of nebulized bronchodilator administrations at the time of initial treatment. The optimum cutoff points for the area under the curve (AUC) were established to predict severe asthma exacerbations.

RESULTS

All 73 episodes of asthma exacerbations (EAEs) in 35 participants were analyzed. Fifty-nine (80.8%) EAEs were classified as severe. Both scales had good significance to recognize the selection of nebulized bronchodilator treatments by AUC of 0.815 (95% Confidence Interval [CI]: 0.680-0.950) in case of BASS, and AUC of 0.822 (95% CI: 0.70-0.944) in case of WDFS. Cutoff points of BASS ≥ 8 had sensitivity 72.9%, specificity 64.3%, positive predictive value (PPV) 89.6%, negative predictive value (NPV) 36.0% at an AUC of 0.718 (95% CI: 0.563-0.873) for severe exacerbations. These results were consistent for cutoff points of WDFS ≥ 5 with sensitivity 78.0%, specificity 50.0%, PPV 86.8%, NPV 35.0% at an AUC of 0.768 (95% CI: 0.650-0.886) for predicting severe exacerbations. There was no significant difference between the AUCs of both scales.

CONCLUSIONS

Both the BASS and WDFS were good and accurate scales and effective screening tools for predicting severe asthma exacerbations in pediatric patients by optimal cutoff points.

The effects of simple graphical and mental visualization of lung sounds in teaching lung auscultation during clinical clerkship: A preliminary study

INTRODUCTION

The study aimed to evaluate visualization-based training's effects on lung auscultation during clinical clerkship (CC) in the Department of Respiratory Medicine on student skills and confidence.

METHODS

The study period was December 2020-November 2021. Overall, 65 students attended a lecture on lung auscultation featuring a simulator (Mr. Lung™). Among them, 35 (visualization group) received additional training wherein they were asked to mentally visualize lung sounds using a graphical visualized lung sounds diagram as an example. All students answered questions on their self-efficacy regarding lung auscultation before and after four weeks of CC. They also took a lung auscultation test with the simulator at the beginning of CC (pre-test) and on the last day of the third week (post-test) (maximum score: 25). We compared the answers in the questionnaire and the test scores between the visualization group and students who only attended the lecture (control group, n = 30). The Wilcoxon signed-rank test and analysis of covariance were used to compare the answers to the questionnaire about confidence in lung auscultation and the scores of the lung auscultation tests before and after the training.

RESULTS

Confidence in auscultation of lung sounds significantly increased in both groups (five-point Likert scale, visualization group: pre-questionnaire median 1 [Interquartile range 1] to post-questionnaire 3 [1], p<0.001; control group: 2 [1] to 3 [1], p<0.001) and was significantly higher in the visualization than in the control group. Test scores increased in both groups (visualization group: pre-test 11 [2] to post-test 15 [4], p<0.001; control group: 11 [5] to 14 [4], p<0.001). However, there were no differences between both groups' pre and post-tests scores (p = 0.623).

CONCLUSION

Visualizing lung sounds may increase medical students' confidence in their lung auscultation skills; this may reduce their resistance to lung auscultation and encourage the repeated auscultation necessary to further improve their long-term auscultation abilities.

An accurate deep learning model for wheezing in children using real world data

Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models require performance improvements and do not reflect the actual clinical situation. We aimed to develop an improved deep-learning model learning to detect wheezing in children, based on data from real clinical practice. In this prospective study, pediatric pulmonologists recorded and verified respiratory sounds in 76 pediatric patients who visited a university hospital in South Korea. In addition, structured data, such as sex, age, and auscultation location, were collected. Using our dataset, we implemented an optimal model by transforming it based on the convolutional neural network model. Finally, we proposed a model using a 34-layer residual network with the convolutional block attention module for audio data and multilayer perceptron layers for tabular data. The proposed model had an accuracy of 91.2%, area under the curve of 89.1%, precision of 94.4%, recall of 81%, and F1-score of 87.2%. The deep-learning model proposed had a high accuracy for detecting wheeze sounds. This high-performance model will be helpful for the accurate diagnosis of respiratory diseases in actual clinical practice.

Regularity and mechanism of fake crackle noise in an electronic stethoscope

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

A Rare Case of Kostmann Syndrome Presenting Difficult Airway Challenges and Patient Preparedness for Anesthesiologists

Severe congenital neutropenia (SCN), commonly known as the Kostmann syndrome, is a rare and complex set of disorders defined by a lack of neutrophil maturation in the bone marrow, leading to life-threatening complications. This case report discusses a young adult patient scheduled for elective laparoscopic cholecystectomy. The patient presented with skin lesions which are a common scenario of Kostmann syndrome, but along with that, our patient posed challenges of short neck, limited neck extension, and gynecomastia. These additional conditions dramatically increased the challenges for anesthesiologists to address the anticipated difficult airway. The anticipated difficult airway challenges were handled by following the protocols of difficult airway guidelines 2022. 

Stretchable graded multichannel self-powered respiratory sensor inspired by shark gill

Respiratory sensing provides a simple, non-invasive, and efficient way for medical diagnosis and health monitoring, but it relies on sensors that are conformal, accurate, durable, and sustainable working. Here, a stretchable, multichannel respiratory sensor inspired by the structure of shark gill cleft is reported. The bionic shark gill structure can convert transverse elastic deformation into longitudinal elastic deformation during stretching. Combining the optimized bionic shark gill structure with the piezoelectric and the triboelectric effect, the bionic shark gill respiratory sensor (BSG-RS) can produce a graded electrical response to different tensile strains. Based on this feature, BSG-RS can simultaneously monitor the breathing rate and breathing depth of the human body accurately, and realize the effective recognition of the different human body's breathing state under the supporting software. With good stretchability, wearability, accuracy, and long-term stability (50,000 cycles), BSG-RS is expected to be applied as self-powered smart wearables for mobile medical diagnostic analysis in the future.

Classification of lung sounds using scalogram representation of sound segments and convolutional neural network

Lung auscultation is one of the most common methods for screening of lung diseases. The increasingly high rate of respiratory diseases leads to the need for robust methods to detect the abnormalities in patients' breathing sounds. Lung sounds analysis stands out as a promising approach to automatic screening of lung diseases, serving as a second opinion for doctors as a stand-alone device for preliminary screening of lung diseases in remote areas. In previous research on lung classification using ICBHI Database on Kaggle, lung audios are converted to spectral images and fed into deep neural networks for training. There are a few studies which uses the scalogram, however they focussed on classification among different lung diseases. The use of scalograms in categorising the sound types are rarely used. In this paper, we combined scalograms and neural networks for classification of lung sound types. Padding methods and augmentation are also considered to evaluate the impacts on classification score. An ensemble learning is incorporated to increase classification accuracy by utilising voting of many models. The model trained and evaluated has shown prominent improvement of this method on classification on the benchmark ICBHI database.

The coming era of a new auscultation system for analyzing respiratory sounds

Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.