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Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.

Background: Although crackles on chest auscultation represent a fundamental component of the diagnostic suspect for fibrotic interstitial lung disease (ILD), their reliability has not been properly studied. We assessed the agreement among respiratory physicians on the presence and changes over time of audible crackles collected in a prospective longitudinal cohort of patients with fibrotic ILD., Methods: Lung sounds were digitally recorded at baseline and after 12 months at eight anatomical sites. Nine respiratory physicians blindly assessed randomized couples of recordings obtained from the same anatomical site at different timepoints. The physicians indicated the presence of crackles in individual recordings and which recording from each couple eventually had more intense crackles. Fleiss' kappa coefficient was used to measure inter- and intra-rater agreement., Results: Fifty-two patients, mostly with a diagnosis of IPF (n = 40, 76.9%) were prospectively enrolled between October 2019 and May 2021. The final acoustic dataset included 702 single recordings, corresponding to 351 couples of recordings from baseline and 12-months timepoints. Kappa coefficient was 0.57 (95% CI 0.55-0.58) for the presence of crackles and 0.42 (95% CI 0.41-0.43) for acoustic change. Intra-rater agreement, measured for three respiratory physicians on three repeated assessments, ranged from good to excellent for the presence of crackles (κ = 0.87, κ = 0.86, κ = 0.79), and from moderate to good for acoustic change (κ = 0.75, κ = 0.76, κ = 0.57)., Conclusions: Agreement between respiratory physicians for the presence of crackles and acoustic change was acceptable, suggesting that crackles represent a reliable acoustic finding in patients with fibrotic ILD. Their role as a lung-derived indicator of disease progression merits further studies., (© 2024. The Author(s).)

Objective: A novel automated auscultatory upper-arm cuff blood pressure (BP) monitor for office use (KOROT P3 Accurate, previously InBody BPBIO480KV), which displays Korotkoff sound curves for each BP reading was recently developed. This study investigated whether the review of Korotkoff sound curves by healthcare professionals further improves the accuracy of the device by identifying unreliable BP readings., Methods: Three observers assessed independently the morphology of Korotkoff sound curves of BP measurements obtained during an ISO 81060-2:2018 validation study, and classified them as of good, fair, or poor quality (low amplitude or sound intensity, aberrant morphology, background noise, signal artifact, auscultatory gap, irregular rhythm). The observers were blinded to the study BP measurements., Results: Korotkoff sound curves of 255 BP readings obtained in 85 individuals were analyzed (mean age 57.3 ± 15.0 years, 53 men). Of the SBP readings 80.4/12.2/7.4% were classified as good/fair/poor, and DBP 76.9/12.2/10.9%. Inter-observer agreement in detecting poor-quality curves was 84.7/83.1% (systolic/diastolic). Of poor-quality curves, 10.5/60.7% (systolic/diastolic) clustered in the same individuals. The validation criterion 1 [mean test-reference BP difference ≤5 ± 8 (SD) mmHg] was satisfied for readings with good (0.1 ± 4.9/0.3 ± 3.8 mmHg, systolic/diastolic) and fair-quality curves (-0.4 ± 6.4/0.2 ± 5.0), but not for poor-quality ones (2.7 ± 8.8/3.6 ± 8.1). By excluding poor-quality readings (40 of 255), criterion 1 of the validation study was improved (0.2 ± 4.9/0.2 ± 3.9 versus 0.3 ± 5.5/0.6 ± 4.7 mmHg)., Conclusion: The visual assessment of Korotkoff sounds generated during automated auscultatory BP measurement by the KOROT P3 Accurate professional monitor identifies unreliable readings and further improves the device accuracy., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To describe intrapartum fetal monitoring methods used in all births in Norway in 2019-2020, assess adherence to national guidelines, investigate variation by women's risk status, and explore associations influencing monitoring practices., Methods: A nationwide population-based study. We collected data about all pregnancies with a gestational age ≥ 22 weeks during 2019-2020 from the Medical Birth Registry of Norway. We used descriptive analyses, stratified for risk status, to examine fetal monitoring methods used in all deliveries. Univariable and multivariable logistic regression models were used to determine factors associated with monitoring with cardiotocography (CTG) in low-risk, straightforward births., Results: In total, 14 285 (14%) deliveries were monitored with only intermittent auscultation (IA), 46214 (46%) with only CTG, and 33417 (34%) with IA and CTG combined. Four percent (2 067/50 533) of women with risk factors were monitored with IA only. Half (10589/21 282) of the low-risk women with straightforward births were monitored with CTG. Maternal and fetal characteristics, size of the birth unit and regional practices influenced use of CTG monitoring in this group., Conclusions: Most births are monitored with CTG only, or combined with IA. Half the women with low-risk pregnancies and straightforward births were monitored with CTG although national guidelines recommending IA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational study. The primary objective of the model was to enhance clinical decision-making by providing a more precise assessment of ENAD risk. Methods The study enrolled patients with AP who underwent early enteral nutrition (EN). Real-time collection and analysis of bowel sounds were conducted using an artificial intelligence bowel sounds auscultation system. Univariate analysis, multicollinearity analysis, and logistic regression analysis were employed to identify risk factors associated with ENAD. The random forest algorithm was utilised to establish the prediction model, and partial dependence plots were generated to analyse the impact of risk factors on ENAD risk. Validation of the model was performed using the optimal model Bootstrap resampling method. Predictive performance was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and an area under the receiver operating characteristic (ROC) curve. Results Among the 133 patients included in the study, the incidence of ENAD was 44.4%. Six risk factors were identified, and the model's accuracy was validated through Bootstrap iterations. The prediction accuracy of the model was 81.10%, with a sensitivity of 84.30% and a specificity of 77.80%. The positive predictive value was 82.60%, and the negative predictive value was 80.10%. The area under the ROC curve was 0.904 (95% confidence interval: 0.817-0.997). Conclusion The artificial intelligence bowel sounds auscultation system enhances the assessment of gastrointestinal function in AP patients undergoing EN and facilitates the construction of an ENAD predictive model. The model demonstrates good predictive efficacy, offering an objective basis for precise intervention timing in ENAD management.

Background: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential., Objective: This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats., Methods: A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence)., Results: The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation., Conclusions: To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning., (©Isabelle Ruchonnet-Métrailler, Johan N Siebert, Mary-Anne Hartley, Laurence Lacroix. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.08.2024.)

High-resolution cervical auscultation (HRCA) is an emerging noninvasive and accessible option to assess swallowing by relying upon accelerometry and sound sensors. HRCA has shown tremendous promise and accuracy in identifying and predicting swallowing physiology and biomechanics with accuracies equivalent to trained human judges. These insights have historically been available only through instrumental swallowing evaluation methods, such as videofluoroscopy and endoscopy. HRCA uses supervised learning techniques to interpret swallowing physiology from the acquired signals, which are collected during radiographic assessment of swallowing using barium contrast. Conversely, bedside swallowing screening is typically conducted in non-radiographic settings using only water. This poses a challenge to translating and generalizing HRCA algorithms to bedside screening due to the rheological differences between barium and water. To address this gap, we proposed a cross-domain transformation framework that uses cycle generative adversarial networks to convert HRCA signals of water swallows into a domain compatible with the barium swallows-trained HRCA algorithms. The proposed framework achieved a cross-domain transformation accuracy that surpassed 90%. The authenticity of the generated signals was confirmed using a binary classifier to confirm the framework's capability to produce indistinguishable signals. This framework was also assessed for retaining swallow physiological and biomechanical properties in the signals by applying an existing model from the literature that identifies the opening and closure of the upper esophageal sphincter. The outcomes of this model showed nearly identical results between the generated and original signals. These findings suggest that the proposed transformation framework is a feasible avenue to advance HCRA towards clinical deployment for water-based swallowing screenings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare there are no competing interests., (©2024 Folnožić et al.)

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)

Background and Aims: The management of complications of arteriovenous fistula (AVF) for hemodialysis, principally stenosis, remains a major challenge for clinicians with a substantial impact on health resources. Stenosis not infrequently preludes to thrombotic events with the loss of AVF functionality. A functioning AVF, when listened by a stethoscope, has a continuous systolic-diastolic low-frequency murmur, while with stenosis, the frequency of the murmur increases and the duration of diastolic component decreases, disappearing in severe stenosis. These evidences are strictly subjective and dependent from operator skill and experience. New generation digital stethoscopes are able to record sound and subsequently dedicated software allows to extract quantitative variables that characterize the sound in an absolutely objective and repeatable way. The aim of our study was to analyze with an appropriate software sounds from AVFs taken by a commercial digital stethoscope and to investigate the potentiality to develop an objective way to detect stenosis., Methods: Between September 2022 and January 2023, 64 chronic hemodialysis (HD) patients were screened by two blinded experienced examiners for recognized criteria for stenosis by Doppler ultrasound (DUS) and, consequently, the sound coming from the AVFs using a 3 M™ Littmann® CORE Digital Stethoscope 8570 in standardized sites was recorded. The sound waves were transformed into quantitative variables (amplitude and frequency) using a sound analysis software. The practical usefulness of the core digital stethoscope for a quick identification of an AVF stenosis was further evaluated through a pragmatic trial. Eight young nephrologist trainees underwent a simple auscultatory training consisting of two sessions of sound auscultation focusing two times on a "normal" AVF sound by placing the digital stethoscope on a convenience site of a functional AVF., Results: In 48 patients eligible, all sound components displayed, alone, a remarkable diagnostic capacity. More in detail, the AUC of the average power was 0.872 [95% CI 0.729-0.951], while that of the mean normalized frequency was 0.822 [95% 0.656-0.930]. From a total of 32 auscultations (eight different block sequences, each one comprising four auscultations), the young clinicians were able to identify the correct sound (stenosis/normal AVF) in 25 cases, corresponding to an overall accuracy of 78.12% (95% CI 60.03-90.72%)., Conclusions: The analysis of sound waves by a digital stethoscope permitted us to distinguish between stenotic and no stenotic AVFs. The standardization of this technique and the introducing of data in a deep learning algorithm could allow an objective and fast method for a frequent monitoring of AVF., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

This study evaluated an oscillometric device (OD), Microlife WatchBP Office AFIB, and a hybrid manual auscultatory device (AD), Greenlight 300TM, to determine a suitable blood pressure (BP) measurement device for the Korea National Health and Nutrition Examination Survey in a mercury-free context. Adhering to the 2018 Universal Standard's suggested consensus, the study involved 800 subjects (mean age 51.2 ± 17.5 years; 44.3% male), who underwent triplicate BP measurements following 5 min of rest in a randomized order (OD-first: 398 participants; AD-first: 402 participants). BP difference was calculated as OD value minus AD value, with results stratified by measurement sequence. The overall BP difference and tolerable error probability were -1.1 ± 6.5/-2.6 ± 4.9 mmHg and 89.2%/92.5% for systolic/diastolic BP (SBP/DBP), respectively. Lin's concordance correlation coefficient was 0.907/0.844 for SBP/DBP (OD-first/AD-first: 0.925/0.892 for SBP, 0.842/0.845 for DBP). The overall agreement for hypertension (BP ≥ 140 and/or 90 mmHg) was 0.71 (p < 0.0001), and the OD underestimated the overall hypertension prevalence by 5.1%. Analysis of the AD-first data revealed a lower level of agreement compared to the OD-first data; however, the observed blood pressure difference adhered to Criterion 1 of the 2018 Universal Standard. Microlife met the Criterion 1 of 2018 Universal Standard but underestimated the prevalence of hypertension. The BP discrepancy increased with higher BP levels, male sex, and smaller AC. With increasing age, the discrepancy decreased for SBP and increased for DBP., (© 2024 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC.)

Intermittent auscultation (IA) is an evidence-based method of fetal surveillance during labor for birthing people with low-risk pregnancies. It is a central component of efforts to reduce the primary cesarean rate and promote vaginal birth (American College of Obstetricians and Gynecologists, 2019; Association of Women's Health, Obstetric and Neonatal Nurses, 2022a). The use of intermittent IA decreased with the introduction of electronic fetal monitoring, while the increased use of electronic fetal monitoring has been associated with an increase of cesarean births. This practice monograph includes information on IA techniques; interpretation and documentation; clinical decision-making and interventions; communication; education, staffing, legal issues; and strategies to implement IA., (Copyright © 2023. Published by Elsevier Inc.)

Aim: This study aimed to explore student midwives' theoretical knowledge of intrapartum intermittent auscultation, their confidence in, and their experience of this mode of fetal monitoring., Design and Setting: An online cross-section survey with closed and open questions. Descriptive statistics were used to analyse participants' intermittent auscultation knowledge, confidence, and experience. Reflexive thematic analysis was used to identify patterns within the free text about participants' experiences., Participants: Undergraduate midwifery students (n = 303) from Nursing and Midwifery Council-approved educational institutions within the United Kingdom., Findings: Most participants demonstrated good theoretical knowledge. They had witnessed the technique being used in clinical practice, and when performed, the practice was reported to be in line with national guidance. In closed questions, participants reported feeling confident in their intermittent auscultation skills; however, these data contrasted with free-text responses., Conclusion: This cross-sectional survey found that student midwives possess adequate knowledge of intermittent auscultation. However, reflecting individual clinical experiences, their confidence in their ability to perform intermittent auscultation varied. A lack of opportunity to practice intermittent auscultation, organisational culture, and midwives' preferences have caused student midwives to question their capabilities with this essential clinical skill, leaving some with doubt about their competency close to registration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by a student summer research grant by the Research Council of Lithuania (agreement No. P-SV-22-241). This work was partially supported by grant DITA from funds of Kaunas University of Technology (grant No. PP2023/39/4) and Lithuanian University of Health Sciences., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In the middle third of the 19th century, George Phillip Cammann became known for the stethoscope improvement that came to bear his name and for the development of the then-popular diagnostic technique of auscultatory percussion. During his postgraduate training at the Paris hospitals in 1828-1830, he acquired a special interest in auscultation while attending lectures given by a friend and colleague of Laennec's, French physician Pierre-Charles-Alexandre Louis (1787-1872). In his New York City practice, caring primarily for the working poor, he recognized the need for a better stethoscope and developed a modification that came to bear his name. He conducted research aimed at increasing the accuracy of physical diagnosis by improving and reporting on the technique of auscultatory percussion. An examination of the medical literature, both textbooks and journals, reveals the extent of influence that Cammann had on clinical practice resulting from his contributions to the improvement of the stethoscope and auscultatory percussion., Competing Interests: Declaration of conflicting interestsThe author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: In pediatric patients due to shorter trachea suboptimal positioning of endotracheal tube (ETT) is seen in 20%-50% of patients triggering grave complications. We compared standard auscultation and ultrasound-guided lung sliding to confirm the position of ETT in children., Methods: Seventy-five pediatric patients between 2-8 years of age, American Society of Anesthesiologists physical status classification I and II scheduled for surgery under general anesthesia with endotracheal intubation were studied. The position of ETT was confirmed by chest auscultation for the breath sounds bilaterally and by ultrasound on the same sites for lung-sliding signs. The data obtained were compared to findings on waveform capnography. Study outcomes included position of the ETT; tracheal or esophageal, accuracy of diagnosis and time taken till confirming the diagnosis., Results: Compared to waveform capnography findings, an ultrasound revealed a sensitivity of 94.31%, a specificity of 72.33%, and an accuracy of 90.7% while that for standard auscultation were 82.8%, 47.28%, and 74.67%, respectively. The mean time taken for confirmation of ETT position was significantly shorter with waveform capnography (10.29 ± 2.25 s) compared to auscultation (12.96 ± 2.23 s) and ultrasonography (USG) (14.28 ± 3.77 s)., Conclusion: Confirmation of ETT position using USG or waveform capnography is essential because of high false results using standard auscultation alone. Ultrasound-guided lung sliding method is a simple, fast, and a reliable method used for detecting tube malposition.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In clinical practice, bowel sounds are often used to assess bowel motility. However, the mechanism of bowel-sound occurrence is unknown. Furthermore, there is no objective evidence indicating a relationship between bowel motility and bowel sounds, and diagnoses have been based on empirically established criteria. In this study, simultaneous X-ray fluoroscopy and bowel-sound measurements were used to reveal the mechanism of bowel-sound occurrence. The results indicate that the flow of luminal contents may cause bowel sounds. Additionally, on the basis of the hypothesis that bowel motility recovers with the postoperative course, bowel-sound features that reflect bowel motion were explored, revealing that the current diagnosis indices are appropriate.

Noninvasive blood pressure (NIBP) devices are calibrated against validated auscultation sphygmomanometers using Korotkoff sounds. This study aimed to investigate the timing of Korotkoff sounds in relation to pulse appearance in the brachial artery and values of intra-arterial blood pressure. Experiments were carried out on 15 participants, (14 males, 64.3 ± 10.4 years; one female, 86 yo), undergoing coronary angiography. A conventional occluding cuff, with a microphone for Korotkoff sounds, was placed on the upper arm (on the brachial artery). Intra-arterial blood pressure (IABP) was measured below the cuff with a fluid-filled catheter inserted via the radial artery and an external transducer. Finger photoplethysmography was used to measure brachial pulse wave velocity (PWV). Korotkoff sounds were processed electronically and custom algorithms identified the cuff pressure (CP) at which the first and last Korotkoff sounds were heard. PWV and max slope of the IABP pressure pulse were recorded to estimate arterial stiffness. The brachial artery closed at a CP of 132.0 ± 17.1 mmHg. Systolic and diastolic blood pressure (SBP and DBP) were 147.6 ± 14.3 and 72.7 ± 10.1 mmHg; mean pressure (MP, 100.1 ± 10.4 mmHg) was similar to MP derived from the peak of the oscillogram (98.5 ± 13.6 mmHg). Difference between IABP and CP recorded at first and last occurrence of Korotkoff sounds were, SBP: 19.0 ± 8.3 (range 2-29) mmHg, DBP: 4.0 ± 4.3 (range 2-12) mmHg. SBP derived from the onset of Korotkoff sounds can underestimate IABP by up to 19 mmHg. Since Korotkoff sounds are the recommended method mandated by the universal standard for the validation of blood pressure measuring devices, these errors are propagated through to all NIBP measurement devices irrespective of whether they use auscultatory or oscillometric methods.

The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.

Objective: Although noise is known to negatively affect blood pressure (BP) measurements, its impact on different BP measurement methods remains unclear. The aim of this study is to compare the agreement of oscillometric and auscultatory BP measurement methods under in-ambulance noise levels., Methods: This method-comparison study was conducted on 50 healthy volunteers in a tertiary emergency department (ED). Participants were divided into two groups of 25, and BP was measured using auscultatory and oscillometric methods in noisy and ambient environments by 2 emergency medicine technicians (EMT). The primary object of the study was to compare the agreement of auscultatory mercury sphygmomanometers and automated auscillometric BP measurements in ambient and noisy environments., Results: We examined the agreement between auscultative and oscillometric measurements of BP conducted in an ambient environment (46.75 [IQR (41.2--55.18)] dB) and found that both systolic and diastolic BP were within the level of agreement (LoA) established before the study (systolic BP [-13.96 to 8.48 mmHG], diastolic BP [-7.44 to 8.08 mmHg]); whereas, in noisy environment (92.35 [IQR 88-96.55] dB) both systolic and diastolic BP were outside the range of LoA (systolic BP [-37.77 to 9.94 mmHg], diastolic BP [-21.73 to 16.37 mmHg]). Additionally, we found that in ambient environments, concordance correlation coefficients were higher than in noisy environments (0.943 [0.906-0.966], 0.957 [0.93-0.974]; 0.574 [0.419-0.697], 0.544 [0.326-0.707]; systolic and diastolic BP, respectively)., Conclusion: The results of this study demonstrate that noise significantly affects the agreement between oscillometric and auscultatory blood pressure measurement methods., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Measurement of blood pressure (BP) through manual auscultation and the observation of Korotkoff sounds (KSs) remains the gold standard in BP methodology. Critical to determining BP levels via auscultation is the determination of KS audibility. While absolute sound level audibility is well researched, the problem has not been approached from the point of view of psychoacoustic masking of the sounds. Here, during manual auscultation of BP, a direct comparison is made between what an observer perceives as audible and the electronic analysis of audibility level determined from masking of sound signal levels. KSs are collected during auscultation with an electronic stethoscope, which allows simultaneously observing sound audibility and recording the sound electronically. By time-segmenting the recorded sound around Korotkoff peaks into a test segment and a masking segment, performing Fourier transforms on the segments, and comparing frequency-band sound energy levels, signal-to-noise ratios of a sound to its masking counterpart can be defined. Comparing these ratios to difference limen in the psychoacoustic masking literature, an approximate threshold for sound audibility is obtained. It is anticipated that this approach could have profound effects on future development of automated auscultation BP measurements.

Objective. Cardiopulmonary auscultation is promising to get smart due to the emerging of electronic stethoscopes. Cardiac and lung sounds often appear mixed at both time and frequency domain, hence deteriorating the auscultation quality and the further diagnosis performance. The conventional cardiopulmonary sound separation methods may be challenged by the diversity in cardiac/lung sounds. In this study, the data-driven feature learning advantage of deep autoencoder and the common quasi-cyclostationarity characteristic are exploited for monaural separation. Approach. Different from most of the existing separation methods that only handle the amplitude of short-time Fourier transform (STFT) spectrum, a complex-valued U-net (CUnet) with deep autoencoder structure, is built to fully exploit both the amplitude and phase information. As a common characteristic of cardiopulmonary sounds, quasi-cyclostationarity of cardiac sound is involved in the loss function for training. Main results . In experiments to separate cardiac/lung sounds for heart valve disorder auscultation, the averaged achieved signal distortion ratio (SDR), signal interference ratio (SIR), and signal artifact ratio (SAR) in cardiac sounds are 7.84 dB, 21.72 dB, and 8.06 dB, respectively. The detection accuracy of aortic stenosis can be raised from 92.21% to 97.90%. Significance . The proposed method can promote the cardiopulmonary sound separation performance, and may improve the detection accuracy for cardiopulmonary diseases., (© 2023 IOP Publishing Ltd.)

Background: Since most developed countries are facing an increase in the number of patients per healthcare worker due to a declining birth rate and an ageing population, relatively simple and safe diagnosis tasks may need to be performed using robotics and automation technologies, without specialists and hospitals. This study presents an automated robotic platform for remote auscultation, which is a highly cost-effective screening tool for detecting abnormal clinical signs., Method: The developed robotic platform is composed of a 6-degree-of-freedom cooperative robotic arm, LiDAR camera, and a spring-based mechanism holding an electric stethoscope. The platform enables autonomous stethoscope positioning based on external body information acquired using the LiDAR camera-based multi-way registration; the platform also ensures safe and flexible contact, maintaining the contact force within a certain range through the passive-actuated mechanism., Results: Our preliminary results confirm that the robotic platform enables estimation of the landing positions required for cardiac examinations based on the depth and landmark information of the body surface. It also handles the stethoscope while maintaining the contact force without relying on the push-in displacement by the robotic arm., Conclusion: The developed robotic platform enables the estimation of the landing positions and handling the stethoscope while maintaining the contact force, which promises the potential of automatic remote auscultation., (© 2022 John Wiley & Sons Ltd.)

This study investigated the reliability and validity (sensitivity and specificity) of cervical auscultation (CA) using both swallow and pre-post swallow-respiratory sounds, as compared with Flexible Endoscopic Evaluation of Swallowing (FEES). With 103 swallow-respiratory sequences from 23 heterogenic patients, these swallows sounds were rated by eight CA-trained Speech-Language Pathologists (SLPs) to investigate: (1) if the swallow was safe (primary outcome); (2) patient dysphagia status; (3) the influence of liquid viscosity on CA accuracy (secondary outcomes). Primary outcome data showed high CA sensitivity (85.4%), and specificity (80.3%) with all consistencies for the safe measurement, with CA predictive values of [Formula: see text] 90% to accurately detect unsafe swallows. Intra-rater reliability was good (Kappa [Formula: see text] 0.65), inter rater reliability moderate (Kappa [Formula: see text] 0.58). Secondary outcome measures showed high sensitivity (80.1%) to identify if a patient was dysphagic, low specificity (22.9%), and moderate correlation (r s [Formula: see text] 0.62) with FEES. A difference across bolus viscosities identified that CA sensitivities (90.1%) and specificities ([Formula: see text] 84.7%) for thin liquids were greater than for thick liquids (71.0-77.4% sensitivities, 74.0-81.3% specificities). Results demonstrate high validity and moderate-good reliability of CA-trained SLPs to determine swallow safety when compared with FEES. Data support the use of CA as an adjunct to the clinical swallow examination. CA should include pre-post respiratory sounds and requires specific training. Clinical implications: The authors advocate for holistic dysphagia management including instrumental assessment and ongoing CSE/review [Formula: see text] CA. Adding CA to the CSE/review does not replace instrumental assessment, nor should CA be used as a stand-alone tool., (© 2022. The Author(s).)

Use of machine learning to accurately detect aspirating swallowing sounds in children is an evolving field. Previously reported classifiers for the detection of aspirating swallowing sounds in children have reported sensitivities between 79 and 89%. This study aimed to investigate the accuracy of using an automatic speaker recognition approach to differentiate between normal and aspirating swallowing sounds recorded from digital cervical auscultation in children. We analysed 106 normal swallows from 23 healthy children (median 13 months; 52.1% male) and 18 aspirating swallows from 18 children (median 10.5 months; 61.1% male) who underwent concurrent videofluoroscopic swallow studies with digital cervical auscultation. All swallowing sounds were on thin fluids. A support vector machine classifier with a polynomial kernel was trained on feature vectors that comprised the mean and standard deviation of spectral subband centroids extracted from each swallowing sound in the training set. The trained support vector machine was then used to classify swallowing sounds in the test set. We found high accuracy in the differentiation of aspirating and normal swallowing sounds with 98% overall accuracy. Sensitivity for the detection of aspiration and normal swallowing sounds were 89% and 100%, respectively. There were consistent differences in time, power spectral density and spectral subband centroid features between aspirating and normal swallowing sounds in children. This study provides preliminary research evidence that aspirating and normal swallowing sounds in children can be differentiated accurately using machine learning techniques., (© 2022. Crown.)

Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.

There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences (p < 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Auscultation for an extended period of time using a wearable stethoscope enables objective computerized analysis and longitudinal assessment of lung sounds. However, this auscultation method differs from bedside auscultation in that clinicians are not present to optimize the quality of auscultation. No prior studies have compared these two auscultation methods., Objective: The aim of this study was to compare intermittent auscultation using a conventional stethoscope with continuous auscultation using a wearable stethoscope for wheeze detection in patients who present with acute respiratory distress., Methods: Patients presenting to the emergency department with acute respiratory distress were enrolled. The Strados Remote Electronic Stethoscope Platform (RESP™) was used for continuous auscultation, and intermittent auscultation was performed using a U.S. Food and Drug Administration-cleared electronic stethoscope. A recording was made with an electronic stethoscope. Subsequently, continuous recording was made using RESP™, which continued until the patient was admitted or discharged from the emergency department. The number of captured wheezes in each recording was counted and validated by two board-certified physicians., Results: From May 2018 to May 2019, 43 patients were enrolled in the study. Three patients were excluded from analysis due to incomplete audio recording data. The mean length of recording was 62.3 min for continuous auscultation and 0.7 min for intermittent auscultation; 77.5% (31 of 40) of intermittent recordings contained wheezes, in contrast to 85% (34 of 40) of continuous recordings., Conclusions: Extending the duration of auscultation using a wearable stethoscope in a noisy clinical environment showed comparable performance to standard of care intermittent auscultation in identifying patients who have wheezes., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: With the spread of COVID-19, telemedicine has played an important role, but tele-auscultation is still unavailable in most countries. This study introduces and tests a tele-auscultation system (Stemoscope) and compares the concordance of the Stemoscope with the traditional stethoscope in the evaluation of heart murmurs., Methods: A total of 57 patients with murmurs were recruited, and echocardiographs were performed. Three cardiologists were asked to correctly categorize heart sounds (both systolic murmur and diastolic murmur) as normal vs. abnormal with both the Stemoscope and a traditional acoustic stethoscope under different conditions. Firstly, we compared the in-person auscultation agreement between Stemoscope and the conventional acoustic stethoscope. Secondly, we compared tele-auscultation (recorded heart sounds) agreement between Stemoscope and acoustic results. Thirdly, we compared both the Stemoscope tele-auscultation results and traditional acoustic stethoscope in-person auscultation results with echocardiography. Finally, ten other cardiologists were asked to complete a qualitative questionnaire to assess their experience using the Stemoscope., Results: For murmurs detection, the in-person auscultation agreement between Stemoscope and the acoustic stethoscope was 91% (p = 0.67). The agreement between Stemoscope tele-auscultation and the acoustic stethoscope in-person auscultation was 90% (p = 0.32). When using the echocardiographic findings as the reference, the agreement between Stemoscope (tele-auscultation) and the acoustic stethoscope (in-person auscultation) was 89% vs. 86% (p = 1.00). The system evaluated by ten cardiologists is considered easy to use, and most of them would consider using it in a telemedical setting., Conclusion: In-person auscultation and tele-auscultation by the Stemoscope are in good agreement with manual acoustic auscultation. The Stemoscope is a helpful heart murmur screening tool at a distance and can be used in telemedicine., (© 2022. The Author(s).)

Intermittent auscultation (IA) of fetal heart has become acceptable in low risk labors even in the developed countries. However, the instances of birth asphyxia occur despite adhering to the guidelines. Such outcomes need not be the inherent limitations of IA, but improvements in the IA regime are highly desirable. The systematic analyses of available studies have been unhelpful to ascertain an optimal regime or suggest improvements. This analytical review uses detailed modeling and reasoning to examine/propose safe and effective regime. It counters a misconception that the Doppler-device is not superior to Pinard stethoscope in usability, accuracy and thereby decision making. Importantly, the Doppler-device should not be used to actually count the fetal heart tones (like a Pinard stethoscope) as insisted by many guidelines. The review demonstrates that counting to 120-160 over a minute is arduous, superfluous and fraught with fallacies and risks. Observation of the digital read-out of the fetal heart rate (FHR) and its trend during the auscultation duration is far more informative. IA should focus on the two FHR parameters namely the baseline and late decelerations. Detection of additional FHR changes like overshoots, cycling or accelerations do not add value. Doppler-device FHR readouts over a steady pattern (commonly just before the contraction) best represent the baseline. FHR observation (IA) should commence in the later part of the contraction and continue till the beginning of next contraction and need not arbitrarily end at 1 min (a legacy of preoccupation with actual counting). Heightened awareness is required to detect late decelerations at the end of contractions. It would suffice to perform IA over a couple of contractions every 20-30 min during the first stage of labor. This improved methodology would avoid mistakes and improve the detection of FHR abnormalities to enhance patient safety in future practice guidelines.

Purpose: The problems posed by trauma, fractures, and dislocations have not changed in human history. The traumas of prehistoric persons were similar to those observed by Imhotep, Hippocrates, and Galen or, more recently, by Ambroise Paré, Watson Jones, and Böhler. And the current road traumas are probably no more severe than those caused by mammoths, the construction of the pyramids, or middle age wars. Diagnostic methods have evolved, and the advent of radiography has revolutionized the diagnosis of traumatology. Before discovering radiography, another physical phenomenon made it possible to help in the diagnosis of fractures. This physical phenomenon is acoustic., Methods: Curiously, no history of acoustics in fracture diagnosis has been published so far. This article proposes briefly reviewing the history and evolution of acoustics in orthopaedic surgery from antiquity to the present day., Results: Before the invention of radiography by Conrad Roentgen in 1895, the surgeons described crepitus as the most critical sign of fractures in antiquity. Surgeons remarked during the eighteenth and nineteenth century that bone was a good sound-conductor. Physicians improved first the diagnosis of fractures by using percussion established by Auenbrugger in 1755. The principle of chest mediate auscultation with a stethoscope was described by Laennec in 1818. Lisfranc used the stethoscope to amplify the crepitus sound of fractures. Surgeons also developed association of percussion and auscultation with a stethoscope to diagnose and reduce fracture. Recently, acoustic emission technology has seen a recent increase in applications to prevent femur fractures during cementless fixation., Conclusion: The acoustic properties of bones were known to a prehistoric person who knew how to make flutes from animal or human bones. Surgeons used them for the diagnosis of fractures before radiography. Acoustic properties of bones currently remain a subject of research for the prevention of fractures., (© 2022. The Author(s) under exclusive licence to SICOT aisbl.)

Objective: This paper proposes a novel framework for lung sound event detection, segmenting continuous lung sound recordings into discrete events and performing recognition of each event., Methods: We propose the use of a multi-branch TCN architecture and exploit a novel fusion strategy to combine the resultant features from these branches. This not only allows the network to retain the most salient information across different temporal granularities and disregards irrelevant information, but also allows our network to process recordings of arbitrary length., Results: The proposed method is evaluated on multiple public and in-house benchmarks, containing irregular and noisy recordings of the respiratory auscultation process for the identification of auscultation events including inhalation, crackles, and rhonchi. Moreover, we provide an end-to-end model interpretation pipeline., Conclusion: Our analysis of different feature fusion strategies shows that the proposed feature concatenation method leads to better suppression of non-informative features, which drastically reduces the classifier overhead resulting in a robust lightweight network., Significance: Lung sound event detection is a primary diagnostic step for numerous respiratory diseases. The proposed method provides a cost-effective and efficient alternative to exhaustive manual segmentation, and provides more accurate segmentation than existing methods. The end-to-end model interpretability helps to build the required trust in the system for use in clinical settings.

Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p < 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy); providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Cervical auscultation has been used clinically as an augmentative procedure for swallow examination. Despite its frequent use for screening and preliminary assessment of swallowing, the usefulness of cervical auscultation is controversial due to a lack of sufficient research, particularly in quantifying swallowing sounds., Aims: To document the acoustic characteristics of normal swallowing sound associated with swallowing bolus of different consistencies among younger healthy adults., Methods & Procedures: A total of 30 healthy young adult participants swallowed 5 ml thickened liquids of four different consistencies (slightly thick, mildly thick, moderately thick and extremely thick) prepared using a starch-based commercial thickener, and the corresponding swallowing sounds were recorded using a wireless accelerometric stethoscope. An array of acoustic measures including duration of swallowing sound (DSS), duration to peak intensity (DPI), frequency at peak intensity (FPI), peak intensity (PI), average intensity (AI), and difference between peak and average intensity (DPAI) values associated with the swallowing signals were obtained., Outcomes & Results: In general, increased durational measures of the swallowing sounds were associated with an increase in bolus consistency. Intensity measures including PI, AI and DPAI were found to be stable across different consistencies. The change in FPI did not appear to be particularly meaningful due to its high variability. In addition, no significant differences were observed between men and women., Conclusions & Implications: Swallow sounds associated with different bolus consistencies could be quantified and used to differentiate consistencies. The present findings could serve as a reference for future swallowing research of normal and dysphagic population., What This Paper Adds: What is already known on the subject Cervical auscultation using traditional stethoscope has been used as part of an informal clinical swallow examination by practitioners. Validity of cervical auscultation is controversial, possibly due to the lack of normative data on swallow sounds. What this paper adds to existing knowledge The present study explored the possibility of using wireless accelerometric stethoscopy for cervical auscultation for dysphagia screening. Acoustic profiles of swallow sounds associated with boluses of different consistencies in healthy individuals were examined. What are the potential or actual clinical implications of this work? Findings contribute to our knowledge about the acoustic characteristics of swallow sounds of boluses of different consistencies in healthy young individuals. The study provides normative clinical data on cervical auscultation using wireless accelerometric stethoscope for normal swallow., (© 2022 Royal College of Speech and Language Therapists.)

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.

Aspiration is a serious complication of swallowing disorders. Adequate detection of aspiration is essential in dysphagia management and treatment. High-resolution cervical auscultation has been increasingly considered as a promising noninvasive swallowing screening tool and has inspired automatic diagnosis with advanced algorithms. The performance of such algorithms relies heavily on the amount of training data. However, the practical collection of cervical auscultation signal is an expensive and time-consuming process because of the clinical settings and trained experts needed for acquisition and interpretations. Furthermore, the relatively infrequent incidence of severe airway invasion during swallowing studies constrains the performance of machine learning models. Here, we produced supplementary training exemplars for desired class by capturing the underlying distribution of original cervical auscultation signal features using auxiliary classifier Wasserstein generative adversarial networks. A 10-fold subject cross-validation was conducted on 2079 sets of 36-dimensional signal features collected from 189 patients undergoing swallowing examinations. The proposed data augmentation outperforms basic data sampling, cost-sensitive learning and other generative models with significant enhancement. This demonstrates the remarkable potential of proposed network in improving classification performance using cervical auscultation signals and paves the way of developing accurate noninvasive swallowing evaluation in dysphagia care.

Objectives: Daily assessments of swallowing function and interventions such as rehabilitation and dietary adjustments are necessary to improve dysphagia. Cervical auscultation is convenient for health care providers for assessing swallowing ability. Although this method allows for swallowing sound evaluations, sensory evaluations with this method are difficult. Thus, we aimed to assess swallowing sound by the combined use of an electronic stethoscope and an artificial intelligence (AI) system that incorporates sound recognition., Material and Methods: Herein, 20 fifth-year dentistry student volunteers were included; each participant was drank 10 ml and then 20 ml of water in different positions (sitting and supine). We developed an algorithm for indexing bolus inflow sounds using AI, which compared the swallowing sounds and created a new index., Results: The new index value used for swallowing sound was significantly higher in men than in women and in the sitting position than in the supine position. A software for acoustic analysis confirmed that the swallowing index was significantly higher in men than in women as well as in the sitting position than in the supine position. These results were similar to those obtained using the new index. However, the new index substantially differed between sexes in terms of posture compared with effective sound pressure., Conclusions: We developed a new algorithm for indexing swallowing sounds using a stethoscope and an AI system, which could identify swallowing sounds. For future research and development, evaluations of patients with dysphagia are necessary to determine the efficacy of the new index for bedside screening of swallowing conditions., (© 2022 The Authors. Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Purpose : Cervical auscultation (CA) uses a stethoscope or microphone to complement the clinical swallow examination by interpreting swallowing sounds and swallow-respiratory coordination. This study investigated the effects of structured CA training on CA-rating agreement with Flexible Endoscopic Evaluation of Swallowing (FEES) and CA rater reliability. Method : Thirty-nine speech-language pathologists participated in a structured CA training course at Gothenburg University. They rated nine swallow-respiratory sound recordings which were simultaneously recorded during FEES. Swallow sounds were rated six weeks prior to the CA-workshop using two binary yes/no questions, (1) Safe , (2) Dysphagia , and a third Dysphagia Severity rating. Swallow sounds were rated again (re-randomised) one month post CA-workshop. Result : Agreement with FEES (validity) improved significantly ( p  < 0.05) pre-post training for the Safe and Dysphagia questions, with post training sensitivities >90% and specificities at 76% and 85% respectively. Dysphagia severity rating improved non-significantly. Intra-rater reliability improved significantly with kappa statistics >0.90 post training. Improvements for inter-rater reliability were noted, though non-significant. Conclusion : Results demonstrate that with structured training, the validity of CA (to detect a Safe/Dysphagic swallow) significantly improves, as does intra-rater reliability. This is congruent with literature identifying the positive effects of structured training improving instrumental dysphagia assessment.

Background: Auscultation is an easy way to evaluate and diagnose patients with lung conditions but has the shortcoming of being subjective. Using the spectrogram, it is possible to visualize wheezing. We therefore conducted a study to compare the efficacy of diagnosing wheezing by auscultation versus diagnosing wheezing by spectrogram., Methods: This was an investigation of interrater reliability and agreement in which the subject population consisted of children, and the rater population consisted of pediatric pulmonologists. We recorded 55 respiratory sound files from June to November 2019. Three pediatric pulmonologists listened to the respiratory sound files and assessed whether wheezing was present. All respiratory sound files were also converted into spectrograms; the same pulmonologists viewed these and assessed whether wheezing was present. We tested for interrater reliability and agreement between the auscultation results and spectrographic results and investigated the diagnostic reliability of auscultation versus spectrogram., Results: Agreement among the three raters of our auscultation respiratory recordings was 88% and reliability was good (κ = 0.76, P < 0.001). Agreement among the three raters of our spectrograms was 83% and reliability was good (κ =0.66, P < 0.001). The level of agreement between each rater's spectrographic findings and diagnosed wheezing was 91%, 75%, and 93%, respectively. Reliability was accordingly very good, moderate, and very good (κ = 0.82, 0.49, 0.85, P < 0.001), respectively., Conclusions: A spectrogram may be a valuable tool for evaluating wheezing in children. It may also be used to improve a young clinician's ability to accurately diagnose wheezing in the future., (© 2021 Japan Pediatric Society.)

Dysphagia, commonly referred to as abnormal swallowing, affects millions of people annually. If not diagnosed expeditiously, dysphagia can lead to more severe complications, such as pneumonia, nutritional deficiency, and dehydration. Bedside screening is the first step of dysphagia characterization and is usually based on pass/fail tests in which a nurse observes the patient performing water swallows to look for dysphagia overt signs such as coughing. Though quick and convenient, bedside screening only provides low-level judgment of impairment, lacks standardization, and suffers from subjectivity. Recently, high resolution cervical auscultation (HRCA) has been investigated as a less expensive and non-invasive method to diagnose dysphagia. It has shown strong preliminary evidence of its effectiveness in penetration-aspiration detection as well as multiple swallow kinematics. HRCA signals have traditionally been collected and investigated in conjunction with videofluoroscopy exams which are performed using barium boluses including thin liquid. An HRCA-based bedside screening is highly desirable to expedite the initial dysphagia diagnosis and overcome all the drawbacks of the current pass/fail screening tests. However, all research conducted for using HRCA in dysphagia is based on thin liquid barium boluses and thus not guaranteed to provide valid results for water boluses used in bedside screening. If HRCA signals show no significant differences between water and thin liquid barium boluses, then the same algorithms developed on thin liquid barium boluses used in diagnostic imaging studies, it can be then directly used with water boluses. This study investigates the similarities and differences between HRCA signals from thin liquid barium swallows compared to those signals from water swallows. Multiple features from the time, frequency, time-frequency, and information-theoretic domain were extracted from each type of swallow and a group of linear mixed models was tested to determine the significance of differences. Machine learning classifiers were fit to the data as well to determine if the swallowed material (thin liquid barium or water) can be correctly predicted from an unlabeled set of HRCA signals. The results demonstrated that there is no systematic difference between the HRCA signals of thin liquid barium swallows and water swallows. While no systematic difference was discovered, the evidence of complete conformity between HRCA signals of both materials was inconclusive. These results must be validated further to confirm conformity between the HRCA signals of thin liquid barium swallows and water swallows.

Background: Despite increasing awareness of swimming-induced pulmonary edema (SIPE), large population-based studies are lacking and the incidence is unknown., Research Question: What is the incidence of SIPE in a mixed group of competitive and recreational swimmers during a large open-water swimming event?, Methods: In four consecutive years (2016-2019), a prospective cohort study was conducted during Sweden's largest open-water swimming event, Vansbrosimningen. All swimmers seeking medical care with acute respiratory symptoms were eligible for the study. SIPE diagnosis was based on clinical findings in 2016 and 2017 and pulmonary edema assessed by lung ultrasound in 2018 and 2019. Data on patient characteristics, clinical findings, and information about the race were collected., Results: Based on 47,573 consecutive swimming distances, 322 patients with acute respiratory symptoms (0.68%; CI, 0.61%-0.75%) were treated at the mobile medical unit. Of these, 211 patients (0.44%; CI, 0.39%-0.51%) received a diagnosis of SIPE. The annual incidence of SIPE was 0.34%, 0.47%, 0.41%, and 0.57%, respectively, from 2016 through 2019. Most patients diagnosed with SIPE were women (90%), despite about equal percentages of men and women participating (47% men and 53% women). The incidence of SIPE overall was 0.75% in women and 0.09% in men. The incidence increased with age, from 0.08% in the youngest age group (18-30 years) to 1.1% in the oldest age group (≥ 61 years). Based on multiple logistic regression analysis, the adjusted odds for SIPE occurring was 8.59 times higher for women compared with men and 12.74 times higher for the oldest age group compared with the youngest age group., Interpretation: The incidence of SIPE over 4 years during a large open-water swimming event in Sweden was 0.44%. The incidence was higher in women than in men and increased with age., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Rene Laennec came up with the idea of a stethoscope in 1816 to avoid the embarrassment of performing immediate auscultation on women. Soon many doctors around the world started using this tool because of its increased accuracy and ease of use. Stethoscopes hold great significance in the medical community. However, is the importance placed on stethoscopes justified today? We now have devices like portable ultrasound machines that make it much easier to visualize the body. These devices offset their higher initial cost by reducing downstream costs due to their greater accuracy and their capability of detecting diseases at an earlier stage. Also, because of the COVID-19 pandemic, new ways are being investigated to reduce the transmission of diseases. Stethoscopes being a possible vector for infectious agents coupled with the advent of newer devices that can visualize the body with greater accuracy put into question the continued use of stethoscopes today. With that said, the use of stethoscopes to diagnose diseases is still crucial in places where buying these new devices is not yet possible. The stethoscope is a great symbol of medicine, but its use needs to be in line with what is best for the patient.

Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases., (© 2021. The Author(s).)

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.

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.

Background: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation., Methods: A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories., Discussion: This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring., Trial Registration: PB&#95;2016-00500, SwissEthics. Registered on 6 April 2020.