The digital stethoscope: Comeback of the phonocardiogram visually
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ABSTRACT:
SUBMITTER: Ingawale S
PROVIDER: S-EPMC8257123 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC8200722 | biostudies-literature
Project description:Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
| S-EPMC4496820 | biostudies-literature
Project description:IntroductionRheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD.Methods and analysisWe aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5-15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet (>1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories-patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing.Ethics and disseminationEthics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators.Article focusThis study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope.
| S-EPMC8344289 | biostudies-literature
Project description:The great majority of genetic disorders are caused by defects in the nuclear genome. However, some significant diseases are the result of mitochondrial mutations. Because of the unique features of the mitochondria, these diseases display characteristic modes of inheritance and a large degree of phenotypic variability. Recent studies have suggested that mitochondrial dysfunction plays a central role in a wide range of age-related disorders and various forms of cancer.
| S-EPMC2225489 | biostudies-literature
Project description:Determination of the relative copy numbers of mixed molecular species in nucleic acid samples is often the objective of biological experiments, including Single-Nucleotide Polymorphism (SNP), indel and gene copy-number characterization, and quantification of CRISPR-Cas9 base editing, cytosine methylation, and RNA editing. Standard dye-terminator chromatograms are a widely accessible, cost-effective information source from which copy-number proportions can be inferred. However, the rate of incorporation of dye terminators is dependent on the dye type, the adjacent sequence string, and the secondary structure of the sequenced strand. These variable rates complicate inferences and have driven scientists to resort to complex and costly quantification methods. Because these complex methods introduce their own biases, researchers are rethinking whether rectifying distortions in sequencing trace files and using direct sequencing for quantification will enable comparable accurate assessment. Indeed, recent developments in software tools (e.g., TIDE, ICE, EditR, BEEP and BEAT) indicate that quantification based on direct Sanger sequencing is gaining in scientific acceptance. This commentary reviews the common obstacles in quantification and the latest insights and developments relevant to estimating copy-number proportions based on direct Sanger sequencing, concluding that bidirectional sequencing and sophisticated base calling are the keys to identifying and avoiding sequence distortions.
| S-EPMC7922598 | biostudies-literature
Project description:Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.
| S-EPMC7180963 | biostudies-literature
Project description:Two hundred years ago Laennec, a young French physician discovered the stethoscope, in order to improve the lung auscultation. The stethoscope became the emblematic appendix of the physicians. Nurses use it also to monitor patients and medical students are proud to use it. We critically review the significance of the stethoscope 200 years after its discovery.Pertinent literature was searched on PubMed and Google about the stethoscope, using also other terms: auscultation, Laennec, medical education, clinical diagnosis. Data were collected in a narrative review.Two centuries after its invention, the stethoscope still remains a major tool in the hands of healthcare professionals. It is routinely used by medical doctors and has become a mark of their status. Nurses use it also to monitor heart rate and blood pressure. Medical students get familiarized to use it during the medical faculty. Patients perceive the stethoscope as an important symbol of the medical profession.Two hundred years ago the stethoscope was invented by Laennec. Despite the advent of X rays, CT, ECG, echocardiography and more recently of the electronic stethoscope, the classical stethoscope invented by Laennec still has its major role in the representation of healthcare providers and is a major utility in clinical diagnosis.
| S-EPMC5536219 | biostudies-other
Project description:Many human activities and states are related to the facial muscles' actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers' expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ?60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).
| S-EPMC7506891 | biostudies-literature
Project description:BackgroundHirsutism is the presence of excess body or facial terminal (coarse) hair growth in females in a male-like pattern, affects 5-15% of women, and is an important sign of underlying androgen excess. Different methods are available for the assessment of hair growth in women.MethodsWe conducted a literature search and analyzed the published studies that reported methods for the assessment of hair growth. We review the basic physiology of hair growth, the development of methods for visually quantifying hair growth, the comparison of these methods with objective measurements of hair growth, how hirsutism may be defined using a visual scoring method, the influence of race and ethnicity on hirsutism, and the impact of hirsutism in diagnosing androgen excess and polycystic ovary syndrome.ResultsObjective methods for the assessment of hair growth including photographic evaluations and microscopic measurements are available but these techniques have limitations for clinical use, including a significant degree of complexity and a high cost. Alternatively, methods for visually scoring or quantifying the amount of terminal body and facial hair growth have been in use since the early 1920s; these methods are semi-quantitative at best and subject to significant inter-observer variability. The most common visual method of scoring the extent of body and facial terminal hair growth in use today is based on a modification of the method originally described by Ferriman and Gallwey in 1961 (i.e. the mFG method).ConclusionOverall, the mFG scoring method is a useful visual instrument for assessing excess terminal hair growth, and the presence of hirsutism, in women.
| S-EPMC2792145 | biostudies-literature
Project description:When people throw or walk to targets in front of them without visual feedback, they often respond short. With feedback, responses rapidly become approximately accurate. To understand this, an experiment is performed with four stages. 1) The errors in blind walking and blind throwing are measured in a virtual environment in light and dark cue conditions. 2) Error feedback is introduced and the resulting learning measured. 3) Transfer to the other response is then measured. 4) Finally, responses to the perceived distances of the targets are measured. There is large initial under-responding. Feedback rapidly makes responses almost accurate. Throw training transfers completely to walking. Walk training produces a small effect on throwing. Under instructions to respond to perceived distances, under-responding recurs. The phenomena are well described by a model in which the relation between target distance and response distance is determined by a sequence of a perceptual, a cognitive, and a motor transform. Walk learning is primarily motor; throw learning is cognitive.
| S-EPMC8142698 | biostudies-literature