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.

Evaluation of a digital stethoscope for electrocardiographic recording in donkeys: Preliminary results.

The digital stethoscope (DS) is a cost-effective single-lead digital stethoscope that allows simultaneous electrocardiographic (ECG) and phonocardiographic recordings on a smartphone. Despite its application in small animals and horses, there are currently no studies on its use in donkeys. The aim of this study was to evaluate the use of a new smartphone-based DS device in recording ECG tracings in donkeys. Standard base-apex lead ECG (sECG) and single-lead DS ECG (dECG) were simultaneously recorded for at least 30 s. Both sECG and dECG tracings were analysed by the same operator, recording heart rate, ECG waves and intervals, and the presence and duration of artefacts. Thirty-seven donkeys were included. The dECG tracings were interpretable in all the animals (100 %). The results showed perfect agreement between the sECG and dECG data for the classification of heart rhythm and P-wave polarity. Strong agreement was found in the evaluation of heart rate calculated manually and automatically by the smartphone app, QRS complex polarity, T wave polarity, and duration of the PR interval. However, no agreement was found in the evaluation of P wave duration, QRS complex duration and amplitude, and T wave duration and amplitude. In conclusion, although this is only a preliminary study, the DS was a valid, practical, and easy to use electrocardiographic tool for recording good-quality ECG tracings to assess the ECGs of donkeys in the field.

SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.

Auscultation is a fundamental diagnostic technique that provides valuable diagnostic information about different parts of the body. With the increasing prevalence of digital stethoscopes and telehealth applications, there is a growing trend towards digitizing the capture of bodily sounds, thereby enabling subsequent analysis using machine learning algorithms. This study introduces the SonicGuard sensor, which is a multichannel acoustic sensor designed for long-term recordings of bodily sounds. We conducted a series of qualification tests, with a specific focus on bowel sounds ranging from controlled experimental environments to phantom measurements and real patient recordings. These tests demonstrate the effectiveness of the proposed sensor setup. The results show that the SonicGuard sensor is comparable to commercially available digital stethoscopes, which are considered the gold standard in the field. This development opens up possibilities for collecting and analyzing bodily sound datasets using machine learning techniques in the future.

Personal stethoscope disinfection practices and bacterial contamination: A cross-sectional study at the University Hospital Emergency Department in Belgrade, Serbia.

BACKGROUND: A significant reduction in bacterial growth on stethoscope membranes has been noticed after performing daily disinfection. Nevertheless, disinfection is rarely performed. We aimed to assess self-reported stethoscope disinfection practices among medical doctors, detect bacterial contamination on personal stethoscopes, and estimate the effectiveness of 70% ethanol as a stethoscope disinfecting agent. METHODS: To determine stethoscope disinfection practices, participants filled out a questionnaire (N = 47), followed by providing stethoscopes for bacterial analysis. Differences in bacterial contamination were observed through the self-reported frequency and method of stethoscope disinfection. The effect of disinfecting with 70% ethanol was evaluated by comparing the presence of bacterial growth before and after disinfection. RESULTS: The presence of bacterial growth was found in 78.7% of the stethoscope samples, with the median (interquartile range) number of colony-forming units at 25 (10-105). The frequency of disinfection greatly impacted the number of colony-forming units, and the method affected the presence of bacterial growth. Disinfection of stethoscope membranes using 70% ethanol resulted in a compelling 97.3% reduction of bacterial growth. CONCLUSIONS: Adequate stethoscope disinfection is highly efficient in reducing bacterial contamination and as such should be considered a critical step in hygienic practices.

AI diagnosis of heart sounds differentiated with super StethoScope.

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope.

PURPOSE: The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection? METHODS: A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.5 months, with increased frequency during exacerbations) performed self-examinations using 3 devices: an artificial intelligence (AI)-aided home stethoscope (providing wheezes, rhonchi, and coarse and fine crackles intensity; respiratory and heart rate; and inspiration-to-expiration ratio), a peripheral capillary oxygen saturation (SpO2) meter, and a peak expiratory flow (PEF) meter and filled out a health state survey. The resulting 6,029 examinations were evaluated by physicians for the presence of exacerbations. For each registered parameter, a machine learning model was trained, and the area under the receiver operating characteristic curve (AUC) was calculated to assess its utility in exacerbation detection. RESULTS: The best single-parameter discriminators of exacerbations were wheezes intensity for young children (AUC 84% [95% CI, 82%-85%]), rhonchi intensity for older children (AUC 81% [95% CI, 79%-84%]), and survey answers for adults (AUC 92% [95% CI, 89%-95%]). The greatest efficacy (in terms of AUC) was observed for a combination of several parameters. CONCLUSIONS: The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier.

Swallowing sound index analysis using electronic stethoscope and artificial intelligence for patients with Parkinson's disease.

BACKGROUND AND PURPOSE: Several noninvasive tools assess swallowing disorders, including electronic stethoscope artificial intelligence (AI) analysis for remote diagnosis, with the potential for telemedicine. This study investigated the swallowing sound index in patients with Parkinson's disease (PD). METHODS: This single-arm, open-label trial assessed the impact of cervical percutaneous interferential current stimulation on swallowing in patients with PD classified as Hoehn-Yahr stages 2-4. Stimulation was conducted for 8 weeks. Baseline data were used to examine the link between the swallowing sound index and indicators such as videofluoroscopy (VF). Furthermore, we examined changes in the swallowing sound index after the intervention. RESULTS: Twenty-five patients were included. The swallowing sound index in patients with PD was higher than that in those with amyotrophic lateral sclerosis but considerably lower than that in healthy controls. The number of patients with normal EAT-10 scores positively correlated with the swallowing sound index, whereas elevated C-reactive protein levels were negatively correlated with the swallowing sound index. However, the index displayed no correlation with other indicators, including the VF results. Despite the intervention, the index remained unchanged throughout the study. CONCLUSION: In patients with PD, a decrease in the swallowing sound index suggests a potential association between swallowing disorders and the risk of aspiration pneumonia. TRIAL REGISTRATION NUMBER: jRCTs062220013.

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 .

Artificial intelligence in medicine: has the time come to hang up the stethoscope?

The question of whether the time has come to hang up the stethoscope is bound up in the promises of artificial intelligence (AI), promises that have so far proven difficult to deliver, perhaps because of the mismatch between the technical capability of AI and its use in real-world clinical settings. This perspective argues that it is time to move away from discussing the generalised promise of disembodied AI and focus on specifics. We need to focus on how the computational method underlying AI, i.e. machine learning (ML), is embedded into tools, how those tools contribute to clinical tasks and decisions and to what extent they can be relied on. Accordingly, we pose four questions that must be asked to make the discussion real and to understand how ML tools contribute to health care: (1) What does the ML algorithm do? (2) How is output of the ML algorithm used in clinical tools? (3) What does the ML tool contribute to clinical tasks or decisions? (4) Can clinicians act or rely on the ML tool? Two exemplar ML tools are examined to show how these questions can be used to better understand the role of ML in supporting clinical tasks and decisions. Ultimately, ML is just a fancy method of automation. We show that it is useful in automating specific and narrowly defined clinical tasks but likely incapable of automating the full gamut of decisions and tasks performed by clinicians.

Use of Digital Stethoscope to Measure Heart Rate in Birds: Comparison of Different Counting Methods Using Phonocardiograms.

The high cardiac contractility of birds poses a challenge to traditional cardiac auscultation, particularly for the accurate determination of heart rate (HR). The objectives of this study were to 1) evaluate the feasibility of using phonocardiograms of adequate length and quality to assess HR in different avian species with a commercially available digital stethoscope, 2) compare 5 counting methods, including 2 direct reading methods (manual counting and using a semiautomatic computerized algorithm as a reference method) and 3 listening methods (progressive mental counting, counting by 10s, and counting with a smartphone application tap counter), and 3) obtain the HR in selected birds and identify a correlation between body weight and HR in different avian species. An inverse correlation on a logarithmic scale was identified between the mean body weight and HR in 60 different bird species (n = 211; R = -0.72, P < 0.0001). Manual reading of phonocardiograms was the most reliable method and had the highest agreement with the reference method; this was followed by the counting by 10s method, the tapping method, and the progressive mental counting method, which was the least reliable. The agreement levels for the different methods were comparable for HRs <200 beats per minute (bpm) in birds weighing >1 kg. For HRs >500 bpm in birds weighing <150 g, only the reading method maintained a good agreement level. A digital stethoscope can be a useful tool for accurately determining the HR in birds, including very small species with high HRs.

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.

StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

The value of the wireless stethoscope in patients with COVID-19 infection in a makeshift hospital.

OBJECTIVE: When COVID-19 sweeps the world, traditional stethoscopes are seen as infectious agents and then the use of stethoscopes is limited especially when health providers were in their personal protective equipment. These reasons led to the ignoring of the values of stethoscopes during pandemics. This study aims to explore the value of wireless stethoscopes in patients of a makeshift hospital. MATERIAL AND METHODS: 200 consecutive hospitalized patients with confirmed SARS-CoV-2 at Lingang Makeshift Hospital in Shanghai, China, were enrolled from April 10 to May 10, 2022 (Trial Registration Number: ChiCTR2000038272,2020/9/15). They were randomly divided into two groups. In group A (n = 100), patients were examined without a stethoscope. In group B (n = 100), lung breath sounds and heart sounds were examined with a wireless stethoscope, and positive signs were recorded. The duration of cough and tachycardia symptoms, as well as emergency cases, were compared between the two groups. In addition, the pressure, anxiety, and depression of patients in the two groups were investigated using the DAS-21 questionnaire scale, to observe the psychological impact of the stethoscope-based doctor-patient communication on patients in the makeshift hospital. RESULTS: There was no significant difference in baseline characteristics between the two groups. In group B, some significant positive signs were detected by wireless stethoscopes, including pulmonary rales and tachycardia, etc. Moreover, the therapeutic measures based on these positive signs effectively alleviated the symptoms of cough and tachycardia, which showed that the duration of symptoms was significantly shorter than that of group A (cough: 2.8 ± 0.9 vs. 3.6 ± 0.9; palpitation: 1.4 ± 0.7 vs. 2.6 ± 0.7). In particular, the number of emergency cases in group B is less than that in group A (1% vs. 3%), and the severity is lower. Notably, stethoscope-based doctor-patient communication was found to be effective in alleviating psychological measures of group B patients. CONCLUSION: Wireless stethoscopes in makeshift hospitals can avoid cross-infections and detect more valuable positive signs, which can help health providers make accurate decisions and relieve patients' symptoms more quickly. Moreover, stethoscope-based doctor-patient communication can diminish the psychological impacts of the epidemic on isolated patients in makeshift hospitals. Trial registration This study was registered in the Chinese Clinical Trial (ChiCTR2000038272) at http://www.chictr.org.cn . http://www.chinadrugtrials.org.cn/clinicaltrials.searchlistdetail.dhtml .

Feasibility of Digital Stethoscopes in Telecardiology Visits for Interstage Monitoring in Infants with Palliated Congenital Heart Disease.

Infants with staged surgical palliation for congenital heart disease are at high-risk for interstage morbidity and mortality. Interstage telecardiology visits (TCV) have been effective in identifying clinical concerns and preventing unnecessary emergency department visits in this high-risk population. We aimed to assess the feasibility of implementing auscultation with digital stethoscopes (DSs) during TCV and the potential impact on interstage care in our Infant Single Ventricle Monitoring & Management Program. In addition to standard home-monitoring practice for TCV, caregivers received training on use of a DS (Eko CORE attachment assembled with Classic II Infant Littman stethoscope). Sound quality of the DS and comparability to in-person auscultation were evaluated based on two providers' subjective assessment. We also evaluated provider and caregiver acceptability of the DS. From 7/2021 to 6/2022, the DS was used during 52 TCVs in 16 patients (median TCVs/patient: 3; range: 1-8), including 7 with hypoplastic left heart syndrome. Quality of heart sounds and murmur auscultation were subjectively equivalent to in-person findings with excellent inter-rater agreement (98%). All providers and caregivers reported ease of use and confidence in evaluation with the DS. In 12% (6/52) of TCVs, the DS provided additional significant information compared to a routine TCV; this expedited life-saving care in two patients. There were no missed events or deaths. Use of a DS during TCV was feasible in this fragile cohort and effective in identifying clinical concerns with no missed events. Longer term use of this technology will further establish its role in telecardiology.

Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review.

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.

Evaluation of a new smartphone-based digital stethoscope featuring phonocardiography and electrocardiography in dogs and cats.

This study assessed a new smartphone-based digital stethoscope (DS) featuring simultaneous phonocardiographic and one-lead electrocardiogram (ECG) recording in dogs and cats. The audio files and ECG traces obtained by the device were compared with conventional auscultation and standard ECG. A total of 99 dogs and nine cats were prospectively included. All cases underwent conventional auscultation using an acoustic stethoscope, standard six-lead ECG, standard echocardiography and recordings with the DS. All the audio recordings, phonocardiographic files and ECG traces were then blind reviewed by an expert operator. The agreement between methods was assessed using Cohen's kappa and the Bland-Altman test. Audio recordings were considered interpretable in 90% animals. Substantial agreement was found in the diagnosis of heart murmur (κ = 0.691) and gallop sound (k = 0.740). In nine animals with an echocardiographic diagnosis of heart disease, only the DS detected a heart murmur or gallop sound. ECG traces recorded with the new device were deemed interpretable in 88 % animals. Diagnosis of heart rhythm showed moderate agreement in the identification of atrial fibrillation (k = 0.596). The detection of ventricular premature complexes and bundle branch blocks revealed an almost perfect agreement (k = 1). Overall, the DS showed a good diagnostic accuracy in detecting heart murmurs, gallop sounds, ventricular premature complexes and bundle branch blocks. A clinically relevant overdiagnosis of atrial fibrillation was found but without evidence of false negatives. The DS could represent a useful screening tool for heart sound abnormalities and cardiac arrhythmias..