Search Results (23)

Background and Objectives: Acute respiratory distress syndrome (ARDS) carries high mortality, with cardiovascular complications frequently contributing to adverse outcomes. This study investigated the relationship between cardiac auscultation using electronic stethoscopy and echocardiographic findings and evaluated their prognostic significance in mechanically ventilated ARDS patients. Materials and Methods: This prospective observational study enrolled 173 consecutive adults with ARDS requiring mechanical ventilation (June 2020–June 2021). Cardiac auscultation was performed using an electronic stethoscope at four standard valvular positions. Bedside echocardiography assessed ventricular function, valvular regurgitation, right ventricular systolic pressure (RVSP), and inferior vena cava dimensions. Primary outcomes were ICU and 90-day mortality; the secondary outcome was ICU length of stay. Results: ICU mortality was 42.2% and 90-day mortality 46.8%. Auscultation findings correlated significantly with echocardiographic parameters: aortic stenosis murmur with an elevated aortic valve velocity (p = 0.009), and mitral/tricuspid regurgitation murmurs with corresponding color Doppler findings (p < 0.001). In multivariate analysis, the mean daily SOFA score (OR 2.39, 95% CI 1.57–3.64, p < 0.001) and RVSP (OR 1.07, 95% CI 1.02–1.11, p = 0.006) independently predicted ICU mortality. For 90-day mortality, the APACHE II score (OR 1.25, p = 0.006), mean daily SOFA score (OR 1.54, p = 0.039), RVSP (OR 1.07, p = 0.020), and mitral regurgitation severity (OR 2.98, p = 0.031) were independent predictors. ICU length of stay was predicted by the mean daily SOFA score (r = 0.35, p < 0.001) and tricuspid regurgitation severity (r = 0.25, p = 0.012). Conclusions: Electronic stethoscope auscultation correlates with the echocardiographic findings in ARDS patients. The RVSP and SOFA scores independently predict mortality, while valvular regurgitation severity provides additional prognostic information for long-term survival and ICU resource utilization. Full article

Background/Objectives: This study aimed to determine the prevalence of persistent symptoms and the radiological and laboratory evolution at 6 months and 5 years after discharge in patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic in Spain and to estimate the healthcare impact of their follow-up. Methods: A retrospective longitudinal observational study was conducted at the “Hospital Central de la Defensa”. A total of 200 patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia were screened. Clinical, radiological, and laboratory data were collected from electronic medical records. Patients with symptoms or radiological abnormalities at discharge underwent in-person evaluations, while the remainder were assessed by telephone. Results: A total of 182 patients met the inclusion and exclusion criteria. Of these, 112 were assessed in the outpatient setting; 60.7% required in-person evaluations, with normal pulmonary auscultation in 93.6%, complete radiological resolution in 85%, and normalized laboratory parameters in almost all cases. At 6 months, 26.5% presented at least one residual symptom, whereas only three patients (4.5%) reported symptoms at 5 years. No risk factors associated with symptom persistence were identified. The estimated cumulative healthcare cost was EUR 21,627.50. Conclusions: Among patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic, 26.7% and 4.46% presented at least one persistent symptom at 6 months and 5 years after discharge, respectively. Full article

Electronic stethoscopes address limitations of auscultation with analog stethoscopes, such as the dependency on the physicians’ hearing ability, their experience, and their subjective interpretation. However, electronic stethoscopes currently found on the commercial market fail to exploit the full potential of cutting-edge microphone technology and innovative multi-sensor approaches. Our novel device, called Open-source Modular Electronic Stethoscope (OMES), proposes a modular upgrade to an analog stethoscope that incorporates multiple sensor types and features microphone array capabilities. OMES has been tested for its performance in detecting heart beats but is designed to be applied to other auscultation sites as well. Above that, it can be employed as an educational and potential research platform to promote the development of revolutionary signal processing techniques and artificial intelligence algorithms. Full article

A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article

Background/Objectives: Electronic fetal monitoring (EFM) has been used for intrapartum fetal surveillance for over 50 years. Despite numerous trials comparing EFM with standard fetal heart rate (FHR) auscultation, it remains contentious whether continuous monitoring with standard interpretation has reliably improved perinatal outcomes, specifically lower rates of perinatal morbidity and mortality. This review examines previous attempts to improve fetal monitoring and presents future directions for novel intrapartum fetal surveillance systems. Methods: We conducted a chronological review of EFM developments, including ancillary methods such as fetal ECG analysis, automated systems for FHR analysis, and artificial intelligence applications. We analyzed the evolution from visual interpretation to intelligent systems and evaluated the performance of various automated monitoring platforms. Results: Various ancillary methods developed to improve EFM accuracy for predicting fetal compromise have shown limited success. Only a limited number of studies demonstrated that adding fetal ECG analysis to visual FHR pattern interpretation resulted in better fetal outcomes. Automated systems for FHR analysis have not consistently enhanced intrapartum fetal surveillance. However, novel approaches such as the Fetal Reserve Index (FRI) show promise by incorporating clinical risk factors with traditional FHR patterns to provide higher-level risk assessment and prognosis. Conclusions: The shortcomings of visual interpretation of FHR patterns persist despite technological advances. Future intelligent intrapartum surveillance systems must combine conventional fetal monitoring with comprehensive risk assessment that incorporates maternal, fetal, and obstetric factors. The integration of artificial intelligence with contextualized metrics like the FRI represents the most promising direction for improving intrapartum fetal surveillance and clinical outcomes. Full article

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. Full article

Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations. Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children. Methods: The methods used were prospective study, with voluntary participation of third-grade primary school children in the region of Crete/Greece, over 6 years (2018–2024). Personal and family history were collected by using a standardized questionnaire and physical evaluation (including weight, height, blood pressure measurement), and cardiac auscultation (digital phonocardiography (PCG)) and 12-lead electrocardiogram (ECG) were recorded at local health stations (Phase I). Following expert verification of responses and obtained data, assisted by designated electronic health record with incorporated decision support algorithms (phase II), pediatric cardiology evaluation at the tertiary referral center followed (phase III). Results: A total of 944 children participated (boys 49.6%). A total of 790 (83.7%) had Phase I referral indication, confirmed in 311(32.9%) during Phase II evaluation. Adiposity (10.8%) and hypertension (3.2%) as risk factors for CVD were documented in 10.8% and 3.2% of the total population, respectively. During Phase III evaluations (n = 201), the majority (n = 132, 14% of total) of children were considered as having a further indication for evaluation by other pediatric subspecialties for their reported symptoms. Abnormal CVD findings were present in 69 (7.3%) of the study population, including minor/trivial structural heart disease in 23 (2.4%) and 17 (1.8%), respectively, referred due to abnormal cardiac auscultation, and ECG abnormalities in 29 (3%), of which 6 (0.6%) were considered potentially significant (including 1 case of genetically confirmed channelopathy-LQT syndrome). Conclusions: CVD screening programs in school children can be very helpful for the early detection of CVD risk factors and of their general health as well. Expert cardiac auscultation and 12-lead ECG allow for the detection of structural and arrhythmogenic heard disease, respectively. Further study is needed regarding performance of individual components, accuracy of interpretation (including computer assisted diagnosis) and cost-effectiveness, before large-scale application of CVD screening in unselected pediatric populations. Full article

This review explores the latest advances in artificial intelligence (AI) and machine learning (ML) for the identification and classification of lung sounds. The article provides a historical overview from the invention of the electronic stethoscope to the auscultation of lung sounds, emphasizing the importance of the rapid diagnosis of lung diseases in the post-COVID-19 era. The review classifies lung sounds, including wheezes and stridors, and explores their pathological relevance. In addition, the article deeply explores feature extraction strategies, measurement methods, and multiple advanced machine learning models for classification, such as deep residual networks (ResNets), convolutional neural networks combined with long short-term memory networks (CNN–LSTM), and transformer models (transformer). The article discusses the problems of insufficient data and replicating human expert experience and proposes future research directions, including improved data utilization, enhanced feature extraction, and classification using spectrograms. Finally, the article emphasizes the expanding role of AI and ML in lung sound diagnosis and their potential for further development in this field. Full article

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. Full article

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. Full article

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

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures. Full article

Electronic auscultation is vital for doctors to detect symptoms and signs of cardiovascular diseases (CVDs), significantly impacting human health. Although progress has been made in heart sound classification, most existing methods require precise segmentation and feature extraction of heart sound signals before classification. To address this, we introduce an innovative approach for heart sound classification. Our method, named Convolution and Transformer Encoder Neural Network (CTENN), simplifies preprocessing, automatically extracting features using a combination of a one-dimensional convolution (1D-Conv) module and a Transformer encoder. Experimental results showcase the superiority of our proposed method in both binary and multi-class tasks, achieving remarkable accuracies of 96.4%, 99.7%, and 95.7% across three distinct datasets compared with that of similar approaches. This advancement holds promise for enhancing CVD diagnosis and treatment. Full article

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. Full article

The COVID-19 pandemic took valuable lives all around the world. The virus was so contagious and lethal that some of the doctors who worked with COVID-19 patients either were seriously infected or died, even after using personal protective equipment. Therefore, the challenge was not only to help communities recover from the pandemic, but also to protect the healthcare staff/professionals. In this regard, this paper presents a comprehensive design of a customized pseudo-humanoid robot to specifically deal with contagious patients by taking basic vitals through a healthcare staff member from a remote location amid the COVID-19 pandemic. The proposed design consists of two portions: (1) a complete design of mechanical, electrical/electronic, mechatronic, control, and communication parts along with complete assembly to make a complete multitask-performing robot that interacts with patients to take vitals, termed as RoboDoc, and (2) the design of the healthcare staff side (master/operator side) control of a joystick mechanism with haptic feedback. The proposed RoboDoc design can be majorly divided into three parts: (1) the locomotion part is composed of two-wheeled DC motors on a rover base and two omni wheels to support the movements of the robot; (2) the interaction part consists of a single degree-of-freedom (s-DOF) neck to have communication with different heights of patients and (3) two anthropomorphic arms with three degrees-of-freedom (3-DOF). These parts help RoboDoc to reach to patient’s location and take all of the vitals using relevant devices such as an IR temperature thermometer, pulse oximeter, and electronic stethoscope for taking live auscultations from the lungs and heart of the patient. The mechanical design was created using solid works, and the electronic control design was made via proteus 8.9. For haptic teleoperation, an XBOX 360 controller based on wireless communication is used at the master/operator side. For the convenience of the healthcare staff (operator), an interactive desktop-based GUI was developed for live monitoring of all the vital signs of patients. For the remote conversation between the healthcare staff and the patient, a tablet is mounted (that also serves as the robot’s face), and that tablet is controlled via a mobile application. For visual aid, a DSLR camera is integrated and controlled remotely, which helps the doctor monitor the patient’s location as well as examine the patient’s throat. Finally, successful experimental results of basic vitals of the remote patient such as temperature sensing, pulse oximeter, and heart rate (using haptic feedback) were obtained to show the significance of the proposed cost-effective RoboDoc design. Full article