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Internal Medicine (Tokyo, Japan) Sep 2019A 79-year-old man with dilated cardiomyopathy and severe functional mitral regurgitation presented with general fatigue and dyspnea. Auscultation revealed a systolic...
A 79-year-old man with dilated cardiomyopathy and severe functional mitral regurgitation presented with general fatigue and dyspnea. Auscultation revealed a systolic regurgitant murmur with a minimized second heart sound due to a low output. On the other hand, the third heart sound was ultimately enhanced, being visible and palpable as a pulsatile knock of the precordium. Phonocardiography and echocardiography successfully confirmed early-diastolic rapid distension of the left ventricle along with rapid ventricular filling and abrupt deceleration of the atrioventricular blood flow to be the precise etiology of the ultimate third heart sound, indicating critically deteriorated hemodynamics due to massive mitral regurgitation combined with a low output.
Topics: Aged; Cardiac Output, Low; Cardiomyopathy, Dilated; Dyspnea; Echocardiography; Fatigue; Heart Auscultation; Heart Sounds; Hemodynamics; Humans; Male; Mitral Valve Insufficiency; Phonocardiography; Ventricular Dysfunction, Left
PubMed: 31118397
DOI: 10.2169/internalmedicine.2731-19 -
ERJ Open Research Oct 2022The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in...
BACKGROUND
The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, "silent" to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis.
METHODS
Lung sound signals were acquired, using a novel full-spectrum (3-2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia.
RESULTS
Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound).
CONCLUSIONS
This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.
PubMed: 36284830
DOI: 10.1183/23120541.00152-2022 -
Cureus Jul 2023This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, evaluates the effectiveness of... (Review)
Review
This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, evaluates the effectiveness of simulation-based education in teaching cardiac auscultation. A team of researchers conducted a comprehensive, systematic search of the PubMed database from 2010 to 2021, focusing on cardiac auscultation, education, proficiency, and students. After rigorous filtering, a total of 14 articles, primarily involving medical students and residents, met the inclusion criteria. The articles were categorized based on their focus areas: diagnostic accuracy, knowledge acquisition, competency, and learner satisfaction. Findings suggest that the majority of the studies (86% or 12 out of 14) reported positive outcomes of using simulation for teaching cardiac auscultation, demonstrating improvements in the identified focus areas across diverse contexts. The review underscores the need for future research to further standardize simulation teaching practices, aiming to reduce costs, improve usability, and possibly incorporate multiple simulation approaches in a universal educational process. This approach could enhance outcomes across varied fields and learning styles.
PubMed: 37554623
DOI: 10.7759/cureus.41567 -
Animals : An Open Access Journal From... Dec 2021Heart murmurs are detected frequently when auscultating horses and certain murmurs can usually be linked to specific valvular regurgitations. Limited information exists...
Heart murmurs are detected frequently when auscultating horses and certain murmurs can usually be linked to specific valvular regurgitations. Limited information exists about the accuracy of these broad rules in warmblood horses and the influence of grade of the regurgitation and dimensional changes on murmur intensity. This study aims to clarify the accuracy of cardiac auscultation in warmblood horses and the influence of the grade of regurgitation and dimensional changes on the loudness of the murmur. In this retrospective study, 822 warmblood horses presented for cardiac examination in a large equine referral center in northern Germany underwent a thorough cardiac auscultation. In total, 653 of these revealed one or more heart murmurs. Most common auscultatory findings were left-sided systolic murmurs (68%) or left-sided diastolic murmurs (15%). On 635 of these horses, an echocardiographic examination was performed, revealing regurgitations of the mitral valve as the most common valvular regurgitation (77%) followed by regurgitations of the aortic valve (23%). Thirty-one percent of horses that underwent echocardiographic examination displayed dimensional changes of one or more compartments of the heart, with the left atrium being most affected (21%), followed by the left ventricle (13%). The main goal of this study was to link certain auscultatory findings with results of the echocardiographic examinations, trying to determine whether auscultation and echocardiography agreed on the valve affected, as well as to find out if loudness of the murmur coincided with grade of regurgitation and presence of dimensional changes. Agreement between auscultation and cardiac ultrasound was substantial (Kappa 0.74) if one or more murmurs and regurgitations were present and almost perfect (Kappa 0.94) if only one murmur and one regurgitation were found. Auscultation was particularly well suited for detection of left-sided systolic and diastolic murmurs, with 87% of left-sided systolic murmurs being caused by a mitral valve regurgitation and 81% of left-sided diastolic murmurs originating from an aortic valve regurgitation. We found a fair agreement between the grade of regurgitation and the respective murmur. Association was particularly good between mild regurgitations and low-grade murmurs, while differentiation between moderate to severe regurgitation based upon the loudness of the murmur was less reliable. Dimensional changes were usually linked to more severe regurgitations and higher-grade murmurs. However, a direct correlation between murmur intensity and the presence or severity of dimensional changes, independent of the grade of valvular regurgitation, could not be established in this cohort of horses.
PubMed: 34944240
DOI: 10.3390/ani11123463 -
Journal of Cardiology Apr 2024In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide.... (Review)
Review
In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.
Topics: Humans; Stethoscopes; Heart Sounds; Artificial Intelligence; Auscultation; Heart Diseases; Heart Auscultation
PubMed: 37734656
DOI: 10.1016/j.jjcc.2023.09.007 -
Scientific Reports Jan 2022A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). The innovations of this system are primarily reflected in the automatic segmentation... (Comparative Study)
Comparative Study
A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). The innovations of this system are primarily reflected in the automatic segmentation and extraction of the first complex sound [Formula: see text] and second complex sound [Formula: see text]; the automatic extraction of the secondary envelope-based diagnostic features [Formula: see text], [Formula: see text], and [Formula: see text] from [Formula: see text] and [Formula: see text]; and the adjustable classifier models that correspond to the confidence bounds of the Chi-square ([Formula: see text]) distribution and are adjusted by the given confidence levels (denoted as [Formula: see text]). The three stages of the proposed system are summarized as follows. In stage 1, the short time modified Hilbert transform (STMHT)-based curve is used to segment and extract [Formula: see text] and [Formula: see text]. In stage 2, the envelopes [Formula: see text] and [Formula: see text] for periods [Formula: see text] and [Formula: see text] are obtained via a novel method, and the frequency features are automatically extracted from [Formula: see text] and [Formula: see text] by setting different threshold value ([Formula: see text]) lines. Finally, the first three principal components determined based on principal component analysis (PCA) are used as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based component objective function [Formula: see text] is generated. Then, the [Formula: see text] distribution for component k is determined by calculating the Mahalanobis distance from [Formula: see text] to the class mean [Formula: see text] for component k, and the confidence region of component k is determined by adjusting the optimal confidence level [Formula: see text] and used as the criterion to diagnose HSs. The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other state-of-the-art methods, and the highest classification accuracies of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 99.67[Formula: see text] and 99.91[Formula: see text] in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting [Formula: see text] to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively.
Topics: Algorithms; Databases, Factual; Heart Auscultation; Heart Diseases; Heart Sounds; Humans; Principal Component Analysis
PubMed: 35079025
DOI: 10.1038/s41598-021-04136-4 -
European Heart Journal. Digital Health Mar 2021Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and...
AIMS
Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create.
METHODS AND RESULTS
Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children's Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age-2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts' face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts' face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84.
CONCLUSIONS
The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts' face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.
PubMed: 36711176
DOI: 10.1093/ehjdh/ztaa017 -
Frontiers in Physiology 2022Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical...
Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.
PubMed: 35237180
DOI: 10.3389/fphys.2022.825612 -
Biomedical Engineering Online Sep 2022With 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...
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.
Topics: Auscultation; COVID-19; Electronics; Heart Auscultation; Heart Murmurs; Humans; Stethoscopes
PubMed: 36068509
DOI: 10.1186/s12938-022-01032-4 -
BMJ Open Mar 2023The objective of this study was to determine the diagnostic accuracy in detecting valvular heart disease (VHD) by heart auscultation, performed by medical doctors.
OBJECTIVE
The objective of this study was to determine the diagnostic accuracy in detecting valvular heart disease (VHD) by heart auscultation, performed by medical doctors.
DESIGN/METHODS
A systematic literature search for diagnostic studies comparing heart auscultation to echocardiography or angiography, to evaluate VHD in adults, was performed in MEDLINE (1947-November 2021) and EMBASE (1947-November 2021). Two reviewers screened all references by title and abstract, to select studies to be included. Disagreements were resolved by consensus meetings. Reference lists of included studies were also screened. The results are presented as a narrative synthesis, and risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2.
MAIN OUTCOME MEASURES
Sensitivity, specificity and likelihood ratios (LRs).
RESULTS
We found 23 articles meeting the inclusion criteria. Auscultation was compared with full echocardiography in 15 of the articles; pulsed Doppler was used as reference standard in 2 articles, while aortography and ventriculography was used in 5 articles. One article used point-of-care ultrasound. The articles were published from year 1967 to 2021. Sensitivity of auscultation ranged from 30% to 100%, and specificity ranged from 28% to 100%. LRs ranged from 1.35 to 26. Most of the included studies used cardiologists or internal medicine residents or specialists as auscultators, whereas two used general practitioners and two studied several different auscultators.
CONCLUSION
Sensitivity, specificity and LRs of auscultation varied considerably across the different studies. There is a sparsity of data from general practice, where auscultation of the heart is usually one of the main methods for detecting VHD. Based on this review, the diagnostic utility of auscultation is unclear and medical doctors should not rely too much on auscultation alone. More research is needed on how auscultation, together with other clinical findings and history, can be used to distinguish patients with VHD.
PROSPERO REGISTRATION NUMBER
CRD42018091675.
Topics: Adult; Humans; Heart Auscultation; Ultrasonography; Auscultation; Echocardiography; Heart Valve Diseases
PubMed: 36963797
DOI: 10.1136/bmjopen-2022-068121