The electronic stethoscope

External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard

OBJECTIVE

There are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD remains unclear. We conducted a comprehensive external evaluation of the Eko murmur analysis software (EMAS) and report its accuracy in detecting murmurs associated with VHD using echocardiography (ECHO) as the reference standard.

METHODS

We analyzed phonocardiogram (PCG) and ECHO data from 1,029 individuals (461 females, mean (SD) age: 61 (29) years, BMI: 29 (9)) at a single academic medical center. PCGs were recorded using the EkoDUO and EkoCORE stethoscopes from the four standard auscultation positions immediately before transthoracic ECHO (TTE) testing. TTE diagnostics were used as reference to calculate the EMAS sensitivity and specificity in detecting murmurs associated with VHD. The 95% confidence intervals are reported.

RESULTS

Of the 4,081 PCGs, 79% were of sufficient quality for murmur analysis. The sensitivity and specificity of the EMAS in detecting VHD were 39.3% (95% CI: 37.2-41.3) and 82.3% (95% CI: 80.0-84.5), respectively. EMAS sensitivity in detecting murmurs associated with common VHD types was 62.5%, 75.0%, 88.9%, and 63.3% for moderate-severe and severe cases of mitral stenosis, aortic regurgitation, aortic stenosis, and mitral regurgitation, respectively.

CONCLUSION

The EMAS algorithm exhibits limited overall sensitivity in detecting VHD. The sensitivity of the algorithm varies across VHD types. These findings suggest that EMAS can be used for diagnosis of specific lesions, but not all VHD types, which limits its clinical applicability as a screening tool.

Prototype of Self-Service Electronic Stethoscope to Be Used by Patients During Online Medical Consultations

This article presents the authors' design of an electronic stethoscope intended for use during online medical consultations for patient auscultation. The goal of the project was to design an instrument that is durable, user-friendly, and affordable. Existing electronic components were used to create the device and a traditional single-sided chest piece. Three-dimensional printing technology was employed to manufacture the prototype. Following the selection of the material, a static tensile strength test was conducted on the printed samples as part of the pre-implementation investigations. Results: Tests on samples made of PLA with a 50% hexagonal infill demonstrated a tensile strength of 36 MPa and an elongation of 4-5%, which was deemed satisfactory for the intended application in the stethoscope's manufacture. The designed and manufactured electronic stethoscope presented in the article can be connected to headphones or speakers, enabling remote medical consultation. According to the opinion of doctors who tested it, it provides the appropriate sound quality for auscultation. This stethoscope facilitates the rapid detection and recognition of cardiac and respiratory activity in humans.

Advances in Wearable Stethoscope Technology: Opportunities for the Early Detection and Prevention of Cardiovascular Diseases

Wearable technology, including devices like Apple and Samsung watches, Fitbits, and smart rings, has become widely popular. However, while these consumer electronics are readily available, they do not yet meet the accuracy and safety standards required for medical devices by the U.S. Food and Drug Administration (FDA). The COVID-19 pandemic has spurred demand for wearable medical devices, particularly those that can support telemedicine and telehealth. Among these, wearable electronic stethoscopes hold significant promise for early detection and prevention of cardiovascular diseases, which remain the leading cause of death globally. This review highlights the potential of wearable electronic stethoscopes to transform cardiovascular health management by enabling early diagnosis and self-monitoring. Additionally, it examines the current challenges and technological advancements needed to overcome them, underscoring the vital role that wearable electronic stethoscopes could play in improving global health outcomes.

Automated analysis of heart sound signals in screening for structural heart disease in children

Our aim was to investigate the ability of an artificial intelligence (AI)-based algorithm to differentiate innocent murmurs from pathologic ones. An AI-based algorithm was developed using heart sound recordings collected from 1413 patients at the five university hospitals in Finland. The corresponding heart condition was verified using echocardiography. In the second phase of the study, patients referred to Helsinki New Children's Hospital due to a heart murmur were prospectively assessed with the algorithm, and then the results were compared with echocardiography findings. Ninety-eight children were included in this prospective study. The algorithm classified 72 (73%) of the heart sounds as normal and 26 (27%) as abnormal. Echocardiography was normal in 63 (64%) children and abnormal in 35 (36%). The algorithm recognized abnormal heart sounds in 24 of 35 children with abnormal echocardiography and normal heart sounds with normal echocardiography in 61 of 63 children. When the murmur was audible, the sensitivity and specificity of the algorithm were 83% (24/29) (confidence interval (CI) 64-94%) and 97% (59/61) (CI 89-100%), respectively.

CONCLUSION

The algorithm was able to distinguish murmurs associated with structural cardiac anomalies from innocent murmurs with good sensitivity and specificity. The algorithm was unable to identify heart defects that did not cause a murmur. Further research is needed on the use of the algorithm in screening for heart murmurs in primary health care.

WHAT IS KNOWN

• Innocent murmurs are common in children, while the incidence of moderate or severe congenital heart defects is low. Auscultation plays a significant role in assessing the need for further examinations of the murmur. The ability to differentiate innocent murmurs from those related to congenital heart defects requires clinical experience on the part of general practitioners. No AI-based auscultation algorithms have been systematically implemented in primary health care.

WHAT IS NEW

• We developed an AI-based algorithm using a large dataset of sound samples validated by echocardiography. The algorithm performed well in recognizing pathological and innocent murmurs in children from different age groups.

MEMS and ECM Sensor Technologies for Cardiorespiratory Sound Monitoring-A Comprehensive Review

This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices (i.e., stethoscopes), which is useful for understanding the theoretical aspects and practical design notes. In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a brief history of stethoscope evolution. Then, we discuss the basic concept of electret condenser microphones (ECMs) and a stethoscope based on them. Then, we discuss the microelectromechanical systems (MEMSs) technology, particularly focusing on piezoelectric transducer sensors. This paper comprehensively reviews sensing technologies for cardiorespiratory auscultation, emphasizing MEMS-based wearable designs in the past decade. To our knowledge, this is the first paper to summarize ECM and MEMS applications for heart and lung sound analysis.

Comparison of remote and in-person respiratory function grading of brachycephalic dogs

OBJECTIVE

To compare the reliability of respiratory function grading (RFG) scores assigned in-person and remotely via video and electronic stethoscope recordings, evaluated by novice and expert graders.

STUDY DESIGN

Prospective study.

SAMPLE POPULATION

Fifty-seven brachycephalic dogs.

METHODS

Dogs were evaluated in person by expert graders and RFG scores were assigned. Audio and video recordings were made during the in-person evaluations. Four expert and four novice graders evaluated the recordings and assigned an RFG score to each dog. Agreement between in-person and remote RFG scores was assessed using Cohen's kappa statistic. Interobserver reliability was assessed using Fleiss' kappa statistic.

RESULTS

The median RFG score from the in-person assessment was 1 (range, 0-3). Distribution of RFG scores included 12 grade 0 scores, 19 grade 1 scores, 25 grade 2 scores, and 1 grade 3 score. The raw percentage agreements between remote and in-person scores were 68.4%, 59.6%, 64.9%, and 61.4% for the four experts, and 52.6%, 64.9%, 50.9%, and 42.1% for the four novices. Reliability between remote and in-person RFG scores was poor to moderate both for the experts (Cohen's kappa: .48, .37, .46, .41) and novices (Cohen's kappa: .28, .47, .28, .21). Interobserver reliability was moderate among the experts (Fleiss' kappa: .59) and poor among the novices (Fleiss' kappa: .39).

CONCLUSION

Remote RFG scores had poor to moderate interassessment and interobserver reliability. Novice evaluators performed worse than experts for remote or in-person RFG evaluations.

CLINICAL SIGNIFICANCE

Remote RFG, as measured in this study, is not reliable for assigning RFG scores. Modifications could be made to remote evaluation to improve reliability. Based upon the performance of novice evaluators, training of evaluators is justified.

The Evolution From Standardized to Virtual Patients in Medical Education

Standardized patients (SPs) are widely used in medical education to teach clinical skills and provide assessments. SPs allow students to practice history taking, physical exams, and communication in controlled settings. However, SPs have limitations such as fatigue, performance variability, and the inability to simulate certain conditions, which virtual patients (VPs) can address. VPs can address these limitations and offer consistency, scalability, and adaptability. Although VPs are being implemented in research settings, they have the potential to be powerful medical education tools. Advancements in immersive technologies such as virtual reality, haptic feedback, and artificial intelligence (AI) will allow the creation of hyper-realistic, interactive training environments that mimic the complexity of real patient encounters. Medical students will be able to engage with VPs in fully immersive settings, complete with haptic feedback and AI-driven dialogue, allowing for more lifelike diagnostic and procedural experiences. The wider availability of such technologies through web services has implications for global medical education and assessment.

Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions.

The Evolving Stethoscope: Insights Derived from Studying Phonocardiography in Trainees

Phonocardiography (PCG) is used as an adjunct to teach cardiac auscultation and is now a function of PCG-capable stethoscopes (PCS). To evaluate the efficacy of PCG and PCS, the authors investigated the impact of providing PCG data and PCSs on how frequently murmurs, rubs, and gallops (MRGs) were correctly identified by third-year medical students. Following their internal medicine rotation, third-year medical students from the Georgetown University School of Medicine completed a standardized auscultation assessment. Sound files of 10 different MRGs with a corresponding clinical vignette and physical exam location were provided with and without PCG (with interchangeable question stems) as 10 paired questions (20 total questions). Some (32) students also received a PCS to use during their rotation. Discrimination/difficulty indexes, comparative chi-squared, and McNemar test p-values were calculated. The addition of phonocardiograms to audio data was associated with more frequent identification of mitral stenosis, S4, and cardiac friction rub, but less frequent identification of ventricular septal defect, S3, and tricuspid regurgitation. Students with a PCS had a higher frequency of identifying a cardiac friction rub. PCG may improve the identification of low-frequency, usually diastolic, heart sounds but appears to worsen or have little effect on the identification of higher-frequency, often systolic, heart sounds. As digital and phonocardiography-capable stethoscopes become more prevalent, insights regarding their strengths and weaknesses may be incorporated into medical school curricula, bedside rounds (to enhance teaching and diagnosis), and telemedicine/tele-auscultation efforts.

Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy

This article reviews technological advances and global trends in the diagnosis, treatment, and monitoring of cardiovascular diseases. A bibliometric analysis was conducted using the SCOPUS database, following PRISMA-ScR guidelines, to identify relevant publications on technologies applied in the diagnosis and treatment of cardiovascular diseases. An increase in scientific output since 2018 was observed, reflecting a growing interest in the technologies available for the treatment of cardiovascular diseases, with terms such as "telemedicine", "artificial intelligence", "image analysis", and "cardiovascular disease" standing out as some of the most commonly used terms in reference to CVDs. Significant trends were identified, such as the use of artificial intelligence in precision medicine and machine learning algorithms to analyse data and predict cardiovascular risk, as well as advances in image analysis and 3D printing. Highlighting the role of artificial intelligence in the diagnosis and continuous monitoring of cardiovascular diseases, showing its potential to improve prognosis and reduce the incidence of acute cardiovascular events, this study presents the integration of traditional cardiology methods with digital health technologies-through a transdisciplinary approach-as a new direction in cardiovascular health, emphasising individualised care and improved clinical outcomes. These advances have great potential to impact healthcare, and as this field expands, it is crucial to understand the current research landscape and direction in order to take advantage of each technological advancement for improving the diagnosis, treatment, and quality of life of cardiovascular patients. It is concluded that the integration of these technologies into clinical practice has important implications for public health. Early detection and personalised treatment of cardiovascular diseases (CVDs) can significantly reduce the morbidity and mortality associated with these diseases. In addition, the optimisation of public health resources through telemedicine and telecare can improve access to quality care. The implementation of these technologies can be a crucial step towards reducing the global burden of cardiovascular diseases.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.

Voice reduction in cardiac auscultation sounds with reference signals measured from vocal resonators

This study proposes the use of vocal resonators to enhance cardiac auscultation signals and evaluates their performance for voice-noise suppression. Data were collected using two electronic stethoscopes while each study subject was talking. One collected auscultation signal from the chest while the other collected voice signals from one of the three voice resonators (cheek, back of the neck, and shoulder). The spectral subtraction method was applied to the signals. Both objective and subjective metrics were used to evaluate the quality of enhanced signals and to investigate the most effective vocal resonator for noise suppression. Our preliminary findings showed a significant improvement after enhancement and demonstrated the efficacy of vocal resonators. A listening survey was conducted with thirteen physicians to evaluate the quality of enhanced signals, and they have received significantly better scores regarding the sound quality than their original signals. The shoulder resonator group demonstrated significantly better sound quality than the cheek group when reducing voice sound in cardiac auscultation signals. The suggested method has the potential to be used for the development of an electronic stethoscope with a robust noise removal function. Significant clinical benefits are expected from the expedited preliminary diagnostic procedure.

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

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

Artificial intelligence in respiratory care: Current scenario and future perspective

BACKGROUND

This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field.

METHODS

A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making.

RESULTS

The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies.

CONCLUSION

By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices.

IMPROVEMENTS

Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.

DEW: A wavelet approach of rare sound event detection

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

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

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

Remote, automatic, digital preanesthetic evaluation - are we there yet?

Recent years have witnessed multiple advancements in the field of information technology in medicine. The need to ensure patient and doctor safety during COVID-19 resulted in improved telemedicine adaptation across various fields, including anaesthesiology. In this review, the authors examine the current state of the elements of preanesthetic evaluation and their remote execution using current and future telemedical facilities and technologies, as well as the potential of future advancements in this field.

Exploring classical machine learning for identification of pathological lung auscultations

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

Design and application of a flexible nano cardiac sound sensor based on P(VDF-TrFE)/KNN/GR composite piezoelectric film for heart disease diagnosis

The paper proposes a flexible micro-nano composite piezoelectric thin film. This flexible piezoelectric film is fabricated through electrospinning process, utilizing a combination of 12 wt% poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)), 8 wt% potassium sodium niobate (KNN) nanoparticles, and 0.5 wt% graphene (GR). Under cyclic loading, the composite film demonstrates a remarkable increase in open-circuit voltage and short-circuit current, achieving values of 36.1 V and 163.7 uA, respectively. These values are 5.8 times and 3.6 times higher than those observed in the pure P(VDF-TrFE) film. The integration of this piezoelectric film into a wearable flexible heartbeat sensor, coupled with the RepMLP classification model, facilitates heartbeat acquisition and real-time automated diagnosis. After training and validation on a dataset containing 2000 heartbeat samples, the system achieved an accuracy of approximately 99% in two classification of heart sound signals (normal and abnormal). This research substantially enhances the output performance of the piezoelectric film, offering a novel and valuable solution for the application of flexible piezoelectric films in physiological signal detection.

Markov-Based Neural Networks for Heart Sound Segmentation: Using Domain Knowledge in a Principled Way

This work considers the problem of segmenting heart sounds into their fundamental components. We unify statistical and data-driven solutions by introducing Markov-based Neural Networks (MNNs), a hybrid end-to-end framework that exploits Markov models as statistical inductive biases for an Artificial Neural Network (ANN) discriminator. We show that an MNN leveraging a simple one-dimensional Convolutional ANN significantly outperforms two recent purely data-driven solutions for this task in two publicly available datasets: PhysioNet 2016 (Sensitivity: 0.947 ±0.02; Positive Predictive Value : 0.937 ±0.025) and the CirCor DigiScope 2022 (Sensitivity: 0.950 ±0.008; Positive Predictive Value: 0.943 ±0.012). We also propose a novel gradient-based unsupervised learning algorithm that effectively makes the MNN adaptive to unseen datum sampled from unknown distributions. We perform a cross dataset analysis and show that an MNN pre-trained in the CirCor DigiScope 2022 can benefit from an average improvement of 3.90% Positive Predictive Value on unseen observations from the PhysioNet 2016 dataset using this method.

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 .

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.

Novel phonocardiography system for heartbeat detection from various locations

The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject's chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.

Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring

Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.

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.

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

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.

Review on the Advancements of Stethoscope Types in Chest Auscultation

Stethoscopes were originally designed for the auscultation of a patient's chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient's illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

Heart function grading evaluation based on heart sounds and convolutional neural networks

Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function assessment methods have their adaptability and limitations. Heart sounds (HS) can reflect changes in heart function. Therefore, HS signals were proposed to assess cardiac function, and a specially designed pruning convolutional neural network (CNN) was applied to recognize subjects' cardiac function at different levels in this paper. Firstly, the adaptive wavelet denoising algorithm and logistic regression based hidden semi-Markov model were utilized for signal denoising and segmentation. Then, the continuous wavelet transform (CWT) was employed to convert the preprocessed HS signals into spectra as input to the convolutional neural network, which can extract features automatically. Finally, the proposed method was compared with AlexNet, Resnet50, Xception, GhostNet and EfficientNet to verify the superiority of the proposed method. Through comprehensive comparison, the proposed approach achieves the best classification performance with an accuracy of 94.34%. The study indicates HS analysis is a non-invasive and effective method for cardiac function classification, which has broad research prospects.

Contactless Stethoscope Enabled by Radar Technology

Contactless vital sign measurement technologies have the potential to greatly improve patient experiences and practitioner safety while creating the opportunity for comfortable continuous monitoring. We introduce a contactless alternative for measuring human heart sounds. We leverage millimeter wave frequency-modulated continuous wave radar and multi-input multi-output beamforming techniques to capture fine skin vibrations that result from the cardiac movements that cause heart sounds. We discuss contact-based heart sound measurement techniques and directly compare the radar heart sound technique with these contact-based approaches. We present experimental cases to test the strengths and limitations of both the contact-based measurement techniques and the contactless radar measurement. We demonstrate that the radar measurement technique is a viable and potentially superior method for capturing human heart sounds in many practical settings.

Development of a Multi-Channel Wearable Heart Sound Visualization System

A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity.

Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram

Background

Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients with faint murmurs or obesity. We aimed to develop an intelligent diagnostic method of detecting heart murmurs in patients with ventricular septal defects (VSDs) and atrial septal defects (ASDs).

Materials and methods

Digital recordings of heart sounds and phonocardiograms of 184 participants were obtained. All participants underwent echocardiography by pediatric cardiologists to determine the type of CHD. The phonocardiogram data were classified as normal, ASD, or VSD. Then, the phonocardiogram signal was used to extract features to construct diagnostic models for disease classification using an advanced optical coherence tomography network (AOCT-NET). Cardiologists were asked to distinguish normal heart sounds from ASD/VSD murmurs after listening to the electronic sound recordings. Comparisons of the cardiologists' assessment and AOCT-NET performance were performed.

Results

Echocardiography results revealed 88 healthy participants, 50 with ASDs, and 46 with VSDs. The AOCT-NET had no advantage in detecting VSD compared with cardiologist assessment. However, AOCT-NET performance was better than that of cardiologists in detecting ASD (sensitivity, 76.4 vs. 27.8%, respectively; specificity, 90 vs. 98.5%, respectively).

Conclusion

The proposed method has the potential to improve the ASD detection rate and could be an important screening tool for patients without symptoms.

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial

The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p > 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p < 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.

Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both of these components are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive noise and convolutional distortion on short-term mel-filterbank energy-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed first on an open-access, multiclass heart sound dataset where we analyzed the effect of additive noise by mixing lung sound noise with the recordings. In noisy conditions, the proposed method outperforms one of the best-performing methods in the literature achieving an Macc (mean of sensitivity and specificity) of 89.55% and an average F-1 score of 82.96%, respectively, when averaged over all noise levels. Next, we perform heart sound abnormality detection (binary classification) experiments on the 2016 Physionet/CinC Challenge dataset that involves noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches on this dataset, in the presence of both additive noise and channel distortion, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains, including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The method thus paves the way toward developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.

Real-world evaluation of the Stemoscope electronic tele-auscultation system

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.

Smartphone as an electronic stethoscope: factors influencing heart sound quality. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:473-480. [PMID: 36712168 PMCID: PMC9708017 DOI: 10.1093/ehjdh/ztac044]

Aims

Smartphones are equipped with a high-quality microphone which may be used as an electronic stethoscope. We aim to investigate the factors influencing quality of heart sound recorded using a smartphone by non-medical users.

Methods and results

An app named Echoes was developed for recording heart sounds using iPhone. Information on phone version and users' characteristics including sex, age, and body mass index (BMI) was collected. Heart sound quality was visually assessed and its relation to phone version and users' characteristics was analysed. A total of 1148 users contributed to 7597 heart sound recordings. Over 80% of users were able to make at least one good-quality recording. Good-, unsure- and bad-quality recordings amounted to 5647 (74.6%), 466 (6.2%) and 1457 (19.2%), respectively. Most good recordings were collected in the first three attempts of the users. Phone version did not significantly change the users' success rate of making a good recording, neither was sex in the first attempt (P = 0.41) or the first three attempts (P = 0.21). Success rate tended to decrease with age in the first attempt (P = 0.06) but not the first three attempts (P = 0.70). BMI did not significantly affect the heart sound quality in a single attempt (P = 0.73) or in three attempts (P = 0.14).

Conclusion

Smartphone can be used by non-medical users to record heart sounds in good quality. Age may affect heart sound recording, but hardware, sex, and BMI do not alter the recording.

Evaluation of the Effect of a Multifunctional Telemedicine Device on Health Care Use and Costs: A Nonrandomized Pragmatic Trial

Background: Telemedicine health insurance models are highly prevalent in the Swiss health care system. Nevertheless, the potential of telemedicine is only partly being achieved, since current telemedicine health insurance models are limited to an initial contact by telephone and a gatekeeper role that organizes access to health care providers such as general practitioners, specialists, or hospitals. Against this background, a telemedicine device with diverse visual and auscultatory examination functions was made available to 2,000 telemedicine-insured clients. This device allowed diagnostic information to be sent to a medical care provider and used for telemedical consultation. Objective: To determine whether the additional implementation of a multifunctional telemedicine examination device resulted in fewer physical consultations, reduced service utilization, and lower health care expenditures among telemedicine-insured clients. Methods: Our analysis is based on claims data from 135,636 clients insured in a telemedicine call center model covering the years 2019 and 2020. We compare the use of health care and health care costs of clients who received a telemedicine device with those without such a device, using multivariable regression to adjust for group differences due to self-selection. Results: We found lower total health care expenditures of -229 (Swiss Francs) and lower inpatient costs of -160 (Swiss Francs) on the part of clients with the telemedicine device. However, the implementation of the telemedicine device did not lead to a statistically significant reduction in service utilization. Conclusions: The treatment of telemedicine-insured clients was on average more cost-effective when they received the multifunctional telemedicine device. Accordingly, complementing the existing telemedicine model with telemedicine devices that allow for improved telemedical consultations has the potential to increase the cost-saving potential of the existing telemedicine call center models.

Impact of coronavirus disease-2019 on pediatric nephrology practice and education: an ESPN survey

BACKGROUND

Coronavirus disease-2019 (COVID-19) has been challenging for patients and medical staff. Radical changes have been needed to prevent disruptions in patient care and medical education.

METHODS

A web-based survey was sent to European Society for Pediatric Nephrology (ESPN) members via the ESPN mailing list to evaluate the effects of the COVID-19 pandemic on delivery of pediatric nephrology (PN) care and educational activities. There were ten questions with subheadings.

RESULTS

Seventy-six centers from 24 countries completed the survey. The time period was between the beginning of the pandemic and May 30, 2020. The number of patients admitted in PN wards and outpatient clinics were significantly decreased (2.2 and 4.5 times, respectively). Telemedicine tools, electronic prescriptions, online applications for off-label drugs, and remote access to laboratory/imaging results were used in almost half of the centers. Despite staff training and protective measures, 33% of centers reported COVID-19 infected staff, and 29% infected patients. Difficulties in receiving pharmaceuticals were reported in 25% of centers. Sixty percent of centers suspended living-related kidney transplantation, and one-third deceased-donor kidney transplantation. Hands-on education was suspended in 91% of medical schools, and face-to-face teaching was replaced by online systems in 85%. Multidisciplinary training in PN was affected in 54% of the centers.

CONCLUSIONS

This survey showed a sharp decline in patient admissions and a significant decrease in kidney transplantation. Telemedicine and online teaching became essential tools, requiring integration into the current system. The prolonged and fluctuating course of the pandemic may pose additional challenges necessitating urgent and rational solutions.

The Intelligibility of the Reversed-Stethoscope Technique in Age-Related Hearing Loss

Background

This study evaluated the effectiveness of the reverse stethoscope technique in improving speech intelligibility. In this technique, a clinician places the earpieces of their stethoscope into the ears of a hearing-impaired patient and speaks into the chest piece.

Methods

The International Speech Test Signal was presented to four Littman® stethoscope models and a Pocketalker® personal voice amplifier using an Audioscan® hearing instrument test box. The acoustic outputs of the stethoscopes and voice amplifier were measured across the frequency spectrum of speech. The Speech Intelligibility Index of the resulting speech was calculated for natural speech and for each device in relation to 10 standardized hearing losses representing the population of older adults.

Results

For each of the 10 hearing losses, the speech signal emitted by the stethoscopes was quieter and yielded lower speech intelligibility scores than regular speech. In contrast, the voice amplifier provided mid- and high-frequency amplification and improved speech intelligibility scores for all but the mildest hearing losses.

Conclusions

The reverse stethoscope technique worsens the clarity of speech and should not be used with older, hearing-impaired patients. Instead, clinicians should use regular speech or, preferably, personal voice amplifiers.

Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review

Background

Frontline health care workers use World Health Organization Integrated Management of Childhood Illnesses (IMCI) guidelines for child pneumonia care in low-resource settings. IMCI guideline pneumonia diagnostic criterion performs with low specificity, resulting in antibiotic overtreatment. Digital auscultation with automated lung sound analysis may improve the diagnostic performance of IMCI pneumonia guidelines. This systematic review aims to summarize the evidence on detecting adventitious lung sounds by digital auscultation with automated analysis compared to reference physician acoustic analysis for child pneumonia diagnosis.

Methods

In this review, articles were searched from MEDLINE, Embase, CINAHL Plus, Web of Science, Global Health, IEEExplore database, Scopus, and the ClinicalTrial.gov databases from the inception of each database to October 27, 2021, and reference lists of selected studies and relevant review articles were searched manually. Studies reporting diagnostic performance of digital auscultation and/or computerized lung sound analysis compared against physicians’ acoustic analysis for pneumonia diagnosis in children under the age of 5 were eligible for this systematic review. Retrieved citations were screened and eligible studies were included for extraction. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. All these steps were independently performed by two authors and disagreements between the reviewers were resolved through discussion with an arbiter. Narrative data synthesis was performed.

Results

A total of 3801 citations were screened and 46 full-text articles were assessed. 10 studies met the inclusion criteria. Half of the studies used a publicly available respiratory sound database to evaluate their proposed work. Reported methodologies/approaches and performance metrics for classifying adventitious lung sounds varied widely across the included studies. All included studies except one reported overall diagnostic performance of the digital auscultation/computerised sound analysis to distinguish adventitious lung sounds, irrespective of the disease condition or age of the participants. The reported accuracies for classifying adventitious lung sounds in the included studies varied from 66.3% to 100%. However, it remained unclear to what extent these results would be applicable for classifying adventitious lung sounds in children with pneumonia.

Conclusions

This systematic review found very limited evidence on the diagnostic performance of digital auscultation to diagnose pneumonia in children. Well-designed studies and robust reporting are required to evaluate the accuracy of digital auscultation in the paediatric population.

A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection

Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance.

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

INTRODUCTION

The WHO's Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations.

METHODS AND ANALYSIS

In a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel's final interpretation as gold standard.

ETHICS AND DISSEMINATION

The study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.

TRIAL REGISTRATION NUMBER

NCT03959956.

Design of a Novel Medical Acoustic Sensor Based on MEMS Bionic Fish Ear Structure

High-performance medical acoustic sensors are essential in medical equipment and diagnosis. Commercially available medical acoustic sensors are capacitive and piezoelectric types. When they are used to detect heart sound signals, there is attenuation and distortion due to the sound transmission between different media. This paper proposes a new bionic acoustic sensor based on the fish ear structure. Through theoretical analysis and finite element simulation, the optimal parameters of the sensitive structure are determined. The sensor is fabricated using microelectromechanical systems (MEMS) technology, and is encapsulated in castor oil, which has an acoustic impedance close to the human body. An electroacoustic test platform is built to test the performance of the sensor. The results showed that the MEMS bionic sensor operated with a bandwidth of 20–2k Hz. Its linearity and frequency responses were better than the electret microphone. In addition, the sensor was tested for heart sound collection application to verify its effectiveness. The proposed sensor can be effectively used in clinical auscultation and has a high SNR.

Evaluation of the electronic stethoscope (FONODOC) as a cardiac screening tool during the preoperative evaluation of children

Background and Aims:

An electronic stethoscope with an inbuilt phonocardiogram is a potentially useful tool for paediatric cardiac evaluation in a resource-limited setting. We aimed to compare the acoustic and electronic stethoscopes with respect to the detection of murmurs as compared to the transthoracic echocardiogram (TTE).

Methods:

This was an observational study. Fifty children aged 0–12 years with congenital heart diseases (CHDs) and 50 without CHD scheduled for echocardiography were examined using both stethoscopes. The findings were corroborated with clinical findings and compared with the echocardiography report.

Results:

Among the 50 cases without CHD, no murmur was detected using either of the stethoscopes. This was in agreement with TTE findings. The calculated specificity of both stethoscopes was 100%. Amongst the 50 cases with CHD, the electronic stethoscope picked up murmurs in 32 cases and missed 18 cases. The acoustic stethoscope picked up murmurs in 29 cases and missed 21 cases. Thus, the sensitivity of electronic and acoustic stethoscopes as compared to TTE was calculated to be 64% and 58%, respectively. The positive predictive value of the electronic stethoscope as compared to TTE was 100% while the negative predictive value was 73%. The kappa statistic was 0.93 suggesting agreement in 93%. Mc-Nemar’s test value was 0.24 suggesting that the electronic stethoscope did not offer any advantage over the acoustic stethoscope for the detection of CHD in children.

Conclusion:

A comparison of the electronic stethoscope with an acoustic stethoscope suggests that the rate of detection of CHD with both stethoscopes is similar and echocardiography remains the gold standard.

Wearable Bluetooth Triage Healthcare Monitoring System

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.