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Journal of the American Heart... May 2021Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning...
Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Cross-Sectional Studies; Deep Learning; Diagnosis, Computer-Assisted; Equipment Design; Female; Heart Auscultation; Heart Murmurs; Humans; Male; Middle Aged; Reproducibility of Results; Stethoscopes; Young Adult
PubMed: 33899504
DOI: 10.1161/JAHA.120.019905 -
Blood Pressure Monitoring Feb 2022Innocent heart murmur is common in healthy infants, children and adolescents. Although most cases are not pathologic, a murmur may be the manifestation of cardiovascular...
BACKGROUND
Innocent heart murmur is common in healthy infants, children and adolescents. Although most cases are not pathologic, a murmur may be the manifestation of cardiovascular disease. It may also cause or be an indicator of blood pressure (BP) and heart rate (HR) changes.
OBJECTIVE
This study aimed to document changes in BP and HR in children with Still's vibratory murmur (SVM).
METHODS
This study included 226 children with SVM, and the control group included 138 healthy children that were age-, height- and weight-balanced. Patient files and our hospital registry system were retrospectively investigated for laboratory findings and electrocardiography and echocardiography results. In addition, we prospectively performed 24-h ambulatory BP monitoring in both groups.
RESULTS
There were no statistically significant differences in 24-h, daytime and nighttime systolic BP, 24-h and nighttime diastolic BP and nighttime HR between the patient and control groups (P = ns). However, daytime diastolic BP, mean HR and daytime HR were significantly higher in patient group (P = 0.009, 0.039 and 0007, respectively).
CONCLUSIONS
We believe that in the presence of a higher HR and a higher aortic diastolic BP, which may induce hemodynamic changes in the left ventricle, flow turbulence through the aortic valve may increase, increasing the probability of hearing a murmur. ambulatory BP monitoring could be useful to obtain a better picture of these parameters during the 24-h period.
Topics: Adolescent; Blood Pressure; Blood Pressure Monitoring, Ambulatory; Child; Circadian Rhythm; Heart Murmurs; Heart Rate; Humans; Hypertension; Retrospective Studies
PubMed: 34992203
DOI: 10.1097/MBP.0000000000000557 -
BioMed Research International 2022Most researchers use features of diastolic murmurs to identify coronary artery disease. However, the diastolic murmurs of coronary artery disease are usually very weak...
Most researchers use features of diastolic murmurs to identify coronary artery disease. However, the diastolic murmurs of coronary artery disease are usually very weak and are easily contaminated by noise and valvular murmurs. Therefore, the diagnostic accuracy of coronary artery disease when only using diastolic murmurs is not well. An algorithm for improving the accuracy in the identification of coronary artery disease by combining the features of the first heart sound and diastolic murmurs was proposed. Firstly, a first heart sound feature extraction algorithm was used to identify coronary artery disease from noncoronary artery disease. Secondly, the Empirical Wavelet Transform algorithm was used to decompose the diastolic heart sound into three modes, and the spectral energy of each mode was calculated to distinguish coronary artery disease from noncoronary artery disease. Then, the features of the fist heart sound, the second diastolic spectral energy, and the parameter P3, which was used to discriminate the diastolic murmurs in coronary artery disease and in valvular disease, were combined together to improve the diagnostic accuracy of coronary artery disease. The comparison experiment results show that the accuracy of the proposed algorithm is superior to some state-of-the-art methods when they are used to diagnose coronary artery disease.
Topics: Algorithms; Coronary Artery Disease; Heart Murmurs; Heart Sounds; Humans; Wavelet Analysis
PubMed: 35252442
DOI: 10.1155/2022/3058835 -
Postgraduate Medicine May 2024This study aimed to assess physicians' approach to cardiac murmurs and their level of knowledge about this sign, which is a crucial finding in childhood cardiac...
OBJECTIVE
This study aimed to assess physicians' approach to cardiac murmurs and their level of knowledge about this sign, which is a crucial finding in childhood cardiac anomalies.
METHODS
The study intended to include all family physicians in the Adıyaman province of Turkey, but ultimately 150 out of 210 physicians participated and was completed with a percentage response rate of 71%. Participants were asked about their approach to cardiac murmurs, answered knowledge questions, and completed a questionnaire on demographic characteristics. Subsequently, eight heart sounds were played, and participants were asked to identify the nature of each sound.
RESULTS
Family medicine specialists (all scores were < 0.001) and physicians who completed a pediatric internship lasting over a month (knowledge score = 0.012, behavioral score = 0.021, recording score = 0.01) demonstrated significantly higher knowledge, approach, and recording scores. Age and years in the profession showed a negative correlation with recording scores.
CONCLUSIONS
The study highlights the significant impact of various factors such as gender, specialization, internship duration, experience, and theoretical knowledge on the ability to recognize and approach cardiac murmurs. These findings underscore the importance of incorporating these factors into medical education and development programs, especially those aimed at improving cardiac examination skills.
Topics: Humans; Male; Female; Heart Murmurs; Clinical Competence; Turkey; Adult; Surveys and Questionnaires; Child; Middle Aged; Health Knowledge, Attitudes, Practice
PubMed: 38805321
DOI: 10.1080/00325481.2024.2360387 -
Journal of Medical Engineering &... Jul 2023Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous...
Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.
Topics: Humans; Signal Processing, Computer-Assisted; Wavelet Analysis; Heart Murmurs; Algorithms; Probability
PubMed: 38393735
DOI: 10.1080/03091902.2024.2310157 -
Journal of Cardiovascular Computed... 2022Computed tomography (CT) imaging is the standard of care before transcatheter aortic valve replacement (TAVR). The aortic annulus undergoes conformational changes during...
BACKGROUND
Computed tomography (CT) imaging is the standard of care before transcatheter aortic valve replacement (TAVR). The aortic annulus undergoes conformational changes during the heart cycle. Therefore, the image acquisition time point can impact prosthesis sizing and fit. Clinical outcome data are lacking. The aim of this study was to compare systolic and diastolic cardiac CT data acquisition with regard to procedural and clinical outcomes in patients undergoing TAVR for severe aortic stenosis (AS).
METHODS
Preprocedural high-pitch helical CT datasets were analyzed in 1954 patients undergoing TAVR between 2013 and 2018 at our center. Patients were stratified into two groups according to the acquisition heart phase (979 systolic and 975 diastolic). The study was approved by the local ethics committee.
RESULTS
Median age was 81.6 [interquartile range 77.5-85.8] years and 964 (49.3%) patients were male. No significant difference was found for the Valve Academic Research Consortium-3 (VARC-3) endpoints of technical failure (systolic, 5.1% vs. diastolic, 5.2%, p = 0.94) or device failure (systolic, 13.7% vs. diastolic, 13.5%, p = 0.92). There was no difference in paravalvular regurgitation. All-cause 30-day mortality was comparable (systolic, 3.6% [95% confidence interval, 2.4-4.7%] vs. diastolic, 3.6% [2.4-4.8%], p = 1.00), while 3-year mortality rates were higher in the diastolic group (Society of Thoracic Surgeons score adjusted hazard ratio, 1.25 [1.07-1.46], p < 0.01).
CONCLUSIONS
While the 30-day technical and clinical outcomes after TAVR are comparable between systolic and diastolic CT imaging, diastolic imaging was associated with higher long-term mortality. Therefore, the data support the guideline recommendation of systolic imaging.
Topics: Aged; Aged, 80 and over; Aortic Valve; Aortic Valve Stenosis; Female; Heart Murmurs; Heart Valve Prosthesis; Humans; Male; Predictive Value of Tests; Risk Factors; Tomography, X-Ray Computed; Transcatheter Aortic Valve Replacement; Treatment Outcome
PubMed: 35637128
DOI: 10.1016/j.jcct.2022.05.003 -
Technology and Culture 2022This article reviews twenty-six volumes of History of Science and Technology in China, a collaborative scholarly work published from 1998 to 2011. The review focuses on... (Review)
Review
This article reviews twenty-six volumes of History of Science and Technology in China, a collaborative scholarly work published from 1998 to 2011. The review focuses on the volumes dealing with the history of technology: Mining and Metallurgy, Machinery, and Transportation. Clearly the series has impacted Chinese literature on the history of technology in China, being the first work to comprehensively and systematically study and expound the history of science and technology in ancient China from the perspective of Chinese scholars.
Topics: Humans; Technology; Publications; China; Transportation; Heart Murmurs
PubMed: 36341613
DOI: 10.1353/tech.2022.0163 -
The Journal of Small Animal Practice Feb 2015
Topics: Animals; Dog Diseases; Female; Heart Murmurs; Male
PubMed: 25627352
DOI: 10.1111/jsap.12334 -
Circulation Journal : Official Journal... Oct 2016
Topics: Echocardiography; Female; Heart Murmurs; Heart Neoplasms; Humans; Middle Aged; Phonocardiography
PubMed: 27581059
DOI: 10.1253/circj.CJ-16-0804 -
Circulation. Cardiovascular Imaging May 2016
Topics: Adult; Echocardiography, Three-Dimensional; Echocardiography, Transesophageal; Female; Heart Murmurs; Heart Septal Defects, Atrial; Humans; Septal Occluder Device
PubMed: 27154075
DOI: 10.1161/CIRCIMAGING.116.004877