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

BACKGROUND: Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers. METHODS: Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level. RESULTS: 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording. CONCLUSIONS: The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

How well can the fetal heart rate baseline be assessed by intrapartum intermittent auscultation? An interrater reliability and agreement study.

BACKGROUND: We aimed to examine the inter-reliability and agreement among midwives when assessing the fetal heart rate (FHR) using the handheld Doppler. The primary aim was to measure the reliability and agreement of FHR baseline (baseline) as beats per minute (bpm). The secondary aims were to measure fluctuations from the baseline, defined as increases and decreases, and classifications (normal or abnormal) of FHR soundtracks. This is the first interrater reliability and agreement study on intermittent auscultation (IA) to our knowledge. METHODS: The participant population consisted of 154 women in labor, from a mixed-risk population and admitted to hospital for intrapartum care. The rater population were 16 midwives from various maternity care settings in Norway. A total of 154 soundtracks were recorded with a handheld Doppler device, and the 16 raters assessed 1-min soundtracks once, through an online survey (Nettskjema). They assessed the baseline, FHR increase or decrease, and the FHR classification. The primary outcome, baseline, was measured with intraclass correlation coefficient (ICC). The secondary outcomes were measured with kappa and proportion of agreement. RESULTS: The interrater reliability for the baseline (bpm) was ICC(A,1) 0.74 (95% CI 0.69-0.78). On average, an absolute difference of 7.9 bpm (95% CI 7.3-8.5 bpm) was observed between pairs of raters. CONCLUSION: Our results demonstrate an acceptable level of reliability and agreement in assessing the baseline using a handheld Doppler.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

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

Evaluation of a digitally enhanced cardiac auscultation learning method in Cameroon: results of a controlled study.

BACKGROUND: Cardiac auscultation is an efficient and effective diagnostic tool, especially in low-income countries where access to modern diagnostic methods remains difficult. This study aimed to evaluate the effect of a digitally enhanced cardiac auscultation learning method on medical students' performance and satisfaction. METHODS: We conducted a double-arm parallel controlled trial, including newly admitted 4th -year medical students enrolled in two medical schools in Yaoundé, Cameroon and allocated into two groups: the intervention group (benefiting from theoretical lessons, clinical internship and the listening sessions of audio recordings of heart sounds) and the control group (benefiting from theoretical lessons and clinical internship). All the participants were subjected to a pretest before the beginning of the training, evaluating theoretical knowledge and recognition of cardiac sounds, and a post-test at the eighth week of clinical training associated with the evaluation of satisfaction. The endpoints were the progression of knowledge score, skills score, total (knowledge and skills) score and participant satisfaction. RESULTS: Forty-nine participants (27 in the intervention group and 22 in the control group) completed the study. The knowledge progression (+ 26.7 versus + 7.5; p ˂0.01) and the total progression (+ 22.5 versus + 14.6; p ˂ 0.01) were higher in the intervention group with a statistically significant difference compared to the control group. There was no significant difference between the two groups regarding skills progression (+ 25 versus + 17.5; p = 0.27). Satisfaction was higher in general in the intervention group (p ˂ 0.01), which recommended this method compared to the control group. CONCLUSION: The learning method of cardiac auscultation reinforced by the listening sessions of audio recordings of heart sounds improves medical students' performances (knowledge and global - knowledge and skills) who find it satisfactory and recommendable. TRIAL REGISTRATION: This trial has been registered the 29/11/2019 in the Pan African Clinical Trials Registry ( http://www.pactr.org ) under unique identification number PACTR202001504666847 and the protocol has been published in BMC Medical Education.

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.

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

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

Cervical Auscultation for Detecting Oropharyngeal Aspiration in Paediatric and Adult Populations: A Systematic Review and Meta-Analysis.

BACKGROUND: Cervical auscultation (CA) involves listening to swallowing and respiratory sounds and/or vibrations to detect oropharyngeal aspiration (OPA). CA has shown promising diagnostic test accuracy when used with the clinical swallowing examination and is gaining popularity in clinical practise. There has not been a review to date analysing the accuracy of CA in paediatric and adult populations with meta-analyses. OBJECTIVES: To determine the accuracy of CA in detecting OPA in paediatric and adult populations, when compared to instrumental assessments. SEARCH METHODS: Databases searched included MEDLINE, PubMed, Embase, CINAHL, AustHealth, Cochrane and Web of Science. The search was restricted between 01 October 2012 and 01 October 2022. SELECTION CRITERIA: Inclusion criteria included (a) all clinical populations of all ages, (b) who have had an instrumental assessment and (c) CA. All study types were included. DATA COLLECTION AND ANALYSIS: Studies were reviewed independently by two authors. The methodological quality of the studies was analysed using the QUADAS-2. MAIN RESULTS: Ten studies met the inclusion criteria for this review and meta-analyses. The pooled diagnostic performance of CA in detecting OPA was 0.91 for sensitivity and 0.79 for specificity. The area under the curve summary receiver operating curve (sROC) was estimated to be 0.86, thereby indicating good discrimination of OPA. Most studies scored high for risk of bias in at least one domain in the QUADAS-2, likely attributed to a lack of high-quality prospectively designed studies. CONCLUSIONS: There are promising diagnostic test accuracies for the use of CA in detection of OPA. Future research could include using CA in specific clinical populations and settings, and identifying standardised criteria for CA.

[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing].

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

[Critical care nurses' decision-making regarding verification of blindly inserted gastric tubes: A cross-sectional questionnaire study]. / Entscheidungsfindung von Intensivpflegefachpersonen zur Verifikation blind applizierter Magensonden.

Critical care nurses' decision-making regarding verification of blindly inserted gastric tubes: A cross-sectional questionnaire study Abstract: Background: The placement and verification of the correct position of blindly applied gastric tubes is regularly performed by nurses in clinical practice. International guidelines recommend a radiological verification as a "first-line" method or if pH measurement is not possible. For Germany, neither evidence-based recommendations nor current data are available. Question: Which methods are used by nurses in German intensive care units for verification of the correct position of blindly applied gastric tubes and how do they assess the reliability of different methods? Methods: Multicenter questionnaire survey. Intensive care units in a non-probability, citeria-based sampling of hospitals in and around Cologne, Germany were included. One nurse was included per participating ward. Analyses were mostly descriptive. Results: In 22 hospitals, 38 wards agreed to participate and 32 (84%) responded to the survey. Auscultation of the upper abdomen with simultaneous air insufflation and aspiration of gastric secretions are frequently used methods for determining the position of gastric tubes. Participants consider auscultation, aspiration of gastric secretions, and radiological control as reliable methods. Conclusions: The findings are in contrast to international recommendations and support the need for evidence-based best practice recommendations and training. Likewise, there is a need for research on feasible bedside methods.

Evaluation of the heart sounds in children using a Doppler Phonolyser.

BACKGROUND: Heart auscultation is an easy and inexpensive tool for early diagnosis of congenital heart defects. In this regard, a simple device which can be used easily by physicians for heart murmur detection will be very useful. The current study was conducted to evaluate the validity of a Doppler-based device named "Doppler Phonolyser" for the diagnosis of structural heart diseases in pediatric patients. In this cross-sectional study, 1272 patients under 16 years who were referred between April 2021 and February 2022, to a pediatric cardiology clinic in Mofid Children Hospital, Tehran, Iran, were enrolled. All the patients were examined by a single experienced pediatric cardiologist using a conventional stethoscope at the first step and a Doppler Phonolyser device at the second step. Afterward, the patient underwent trans-thoracic echocardiography, and the echocardiogram results were compared with the conventional stethoscope as well as the Doppler Phonolyser findings. RESULTS: Sensitivity of the Doppler Phonolyser for detecting congenital heart defects was 90.5%. The specificity of the Doppler Phonolyser in detecting heart disease was 68.9% in compared with the specificity of the conventional stethoscope, which was 94.8%. Among the most common congenital heart defects in our study population, the sensitivity of the Doppler Phonolyser was 100% for detection of tetralogy of Fallot (TOF); In contrast, sensitivity of both the conventional stethoscope and the Doppler Phonolyser was relatively low for detecting atrial septal defect. CONCLUSIONS: Doppler Phonolyser could be useful as a diagnostic tool for the detection of congenital heart defects. The main advantages of the Doppler Phonolyser over the conventional stethoscope are no need for operator experience, the ability to distinguish innocent murmurs from the pathologic ones and no effect of environmental sounds on the performance of the device.

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

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

Reference blood pressure values obtained using the auscultation method for 2-year-old Japanese children: from the Japan Environment and Children's Study.

BACKGROUND: Reference blood pressure (BP) values for Japanese children based on a large number of measurements by auscultation have not yet been established. METHODS: This was a cross-sectional analysis of data from a birth-cohort study. The data from the sub-cohort study conducted for children at the age of 2 years in the Japan Environment and Children's Study from April 2015 to January 2017 were analyzed. BP was measured via auscultation using an aneroid sphygmomanometer. Each participant was measured in triplicate, and the average value of two consecutive measurements with a difference of less than 5 mmHg was recorded. The reference BP values were estimated using the lambda-mu-sigma (LMS) method and compared with those obtained via the polynomial regression model. RESULTS: Data from 3361 participants were analyzed. Although the difference between the estimated BP values by the LMS and the polynomial regression model was small, the LMS model was more valid based on the results of the fit curve of the observed values and regression models for each model. For 2-year-old children with heights in the 50th percentile, the 50th, 90th, 95th, and 99th percentile reference values of systolic BP (mmHg) for boys were 91, 102, 106, and 112, and that for girls were 90, 101, 103, and 109, respectively, and those of diastolic BP for boys were 52, 62, 65, and 71, and that for girls were 52, 62, 65, and 71, respectively. CONCLUSION: The reference BP values for 2-year-old Japanese children were determined based on auscultation and were made available.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.

BACKGROUND: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS: gov Identifier: NCT05318599.

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

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

Suppression of Clothing-Induced Acoustic Attenuation in Robotic Auscultation.

For patients who are often embarrassed and uncomfortable when exposing their breasts and having them touched by physicians of different genders during auscultation, we are developing a robotic system that performs auscultation over clothing. As the technical issue, the sound obtained through the clothing is often attenuated. This study aims to investigate clothing-induced acoustic attenuation and develop a suppression method for it. Because the attenuation is due to the loss of energy as sound propagates through a medium with viscosity, we hypothesized that the attenuation is improved by compressing clothing and shortening the sound propagation distance. Then, the amplitude spectrum of the heart sound was obtained over clothes of different thicknesses and materials in a phantom study and human trial at varying contact forces with a developed passive-actuated end-effector. Our results demonstrate the feasibility of the attenuation suppression method by applying an optimum contact force, which varied according to the clothing condition. In the phantom experiments, the attenuation rate was improved maximumly by 48% when applying the optimal contact force (1 N). In human trials, the attenuation rate was under the acceptable attenuation (40%) when applying the optimal contact force in all combinations in each subject. The proposed method promises the potential of robotic auscultation toward eliminating gender bias.

Heart Sound Classification Network Based on Convolution and Transformer.

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.

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

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment.

Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ±0.5 N. The control strategy is tested in a real environment. The preliminary results show that the control strategy performs well in the constant force-tracking of medical auscultation tasks. The contact force is always within a safe and stable range, and the average contact force is about 5.2 N.

Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach.

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.

A New Method for Heart Disease Detection: Long Short-Term Feature Extraction from Heart Sound Data.

Heart sounds have been extensively studied for heart disease diagnosis for several decades. Traditional machine learning algorithms applied in the literature have typically partitioned heart sounds into small windows and employed feature extraction methods to classify samples. However, as there is no optimal window length that can effectively represent the entire signal, windows may not provide a sufficient representation of the underlying data. To address this issue, this study proposes a novel approach that integrates window-based features with features extracted from the entire signal, thereby improving the overall accuracy of traditional machine learning algorithms. Specifically, feature extraction is carried out using two different time scales. Short-term features are computed from five-second fragments of heart sound instances, whereas long-term features are extracted from the entire signal. The long-term features are combined with the short-term features to create a feature pool known as long short-term features, which is then employed for classification. To evaluate the performance of the proposed method, various traditional machine learning algorithms with various models are applied to the PhysioNet/CinC Challenge 2016 dataset, which is a collection of diverse heart sound data. The experimental results demonstrate that the proposed feature extraction approach increases the accuracy of heart disease diagnosis by nearly 10%.