Auscultation Skills Research Articles

Cardiac murmurs in children.

Approximately 50% of children experience a cardiac murmur at some point in their lives; <1% of these murmurs are attributed to congenital heart disease (CHD). Cardiac murmur might be the first clinical sign of a significant CHD in children. Despite careful routine medical examinations at birth, approximately 50% of CHD cases could remain unrecognised. Cardiovascular symptoms and signs could be specific or non-specific in neonates and children with heart murmurs. Knowledge about red flags in history and physical examinations, and syndromic associations of common CHDs are important. Auscultatory skills to identify systolic, diastolic and continuous murmurs and heart sounds are essential. Differential diagnosis should be formulated based on the location of maximum intensity of murmurs. Younger infants and children with pathological murmurs and red-flag signs should be promptly referred to local paediatric cardiology services for further investigations. Significant skill and knowledge are required for the identification of critical murmurs and associated cardiovascular problems. This review provides a simplified comprehensive update on cardiac murmurs and associated conditions in neonates and children.

The effect of simulation on auscultation skills, self-confidence and anxiety levels in nursing students: A randomized controlled trial

AimThis study evaluated the effect of simulation on auscultation skills, self-confidence and anxiety levels. BackgroundAuscultation is an essential topic in nursing education and patient care. Simulation is efficacious in improving auscultation skills and self-confidence and reducing anxiety levels. It is a valuable educational approach whose effects should be evaluated and disseminated in the context of auscultation. DesignThis study had a randomised controlled trial design. MethodsThe study was conducted with second-year nursing students enrolled at a university in Turkey. One group of students studied auscultation of heart, lung and bowel sounds with a simulated patient (n = 28), the second group with a high fidelity simulator (n = 30) and the third group with traditional education (n = 28). Data were collected using a demographic information form, auscultation control list and Nursing Anxiety and Self-Confidence with Clinical Decision Making Scale. The students' psychomotor auscultation skills, self-confidence and anxiety levels were evaluated. Data on auscultation skills were assessed after the intervention, while data on self-confidence and anxiety levels were collected at baseline, after the intervention and after clinical practices throughout the academic year. ResultsAnxiety and self-confidence levels showed statistically significant differences in intragroup evaluation. The self-confidence levels of the groups working with simulation did not change much in the follow-up test conducted after the end of clinical practices. The mean skill scores in the auscultation control list showed a statistically significant difference between the groups. ConclusionsSimulation and traditional approaches are effective in learning auscultation skills. However, the positive effects of simulation, particularly in enhancing students' self-confidence and reducing anxiety levels, appear to be more permanent and impactful than traditional education. Therefore, it is recommended that simulation be prioritized for teaching auscultation skills.

Simulation-guided auscultatory training before graduation is associated with better auscultatory skills in residents.

A growing body of scientific evidence shows that simulation-guided auscultatory training can significantly improve the skills of medical students. Nevertheless, it remains to be elucidated if this training has any long-term impact on auscultatory skills. We sought to ascertain whether there were differences in heart and lung auscultation among residents who received simulation-guided auscultatory training before graduation vs. those who did not. A total of 43 residents were included in the study; 20 of them entered into Cardiology specialty school (C) and 23 of them entered into Internal and Occupational Medicine specialty schools (M) at the University of Trieste. Based on the history of simulation-guided auscultatory training before graduation (yes = Y; no = N), four groups were formed: CY, CN, MY, and MN. Residents were evaluated in terms of their ability to recognize six heart and five lung sounds, which were reproduced in a random order with the Kyoto-Kagaku patient simulator. Associations between history of simulation training, specialty choice and auscultatory skills were evaluated with Kruskal-Wallis test and logistic regression analysis. Auscultatory skills of residents were associated with simulation-guided training before graduation, regardless of the specialty chosen. Simulation-guided training had a higher impact on residents in Medicine. Overall, heart and lung sounds were correctly recognized in 41% of cases. Logistic regression analysis showed that simulation-guided training was associated with recognition of aortic stenosis, S2 wide split, fine crackles, and pleural rubs. Specialty choice was associated with recognition of aortic stenosis as well as aortic and mitral regurgitation. History of simulation-guided auscultatory training was associated with better auscultatory performance in residents, regardless of the medical specialty chosen. Choice of Cardiology was associated with better scores in aortic stenosis as well as aortic and mitral regurgitation. Nevertheless, overall auscultatory proficiency was quite poor, which suggests that simulation-guided training may help but is probably still too short.

UK student midwives' theoretical knowledge, confidence, and experience of intermittent auscultation of the fetal heart rate during labour: An online cross-sectional survey

AimThis study aimed to explore student midwives' theoretical knowledge of intrapartum intermittent auscultation, their confidence in, and their experience of this mode of fetal monitoring. Design and SettingAn online cross-section survey with closed and open questions. Descriptive statistics were used to analyse participants' intermittent auscultation knowledge, confidence, and experience. Reflexive thematic analysis was used to identify patterns within the free text about participants' experiences. ParticipantsUndergraduate midwifery students (n = 303) from Nursing and Midwifery Council-approved educational institutions within the United Kingdom. FindingsMost participants demonstrated good theoretical knowledge. They had witnessed the technique being used in clinical practice, and when performed, the practice was reported to be in line with national guidance. In closed questions, participants reported feeling confident in their intermittent auscultation skills; however, these data contrasted with free-text responses. ConclusionThis cross-sectional survey found that student midwives possess adequate knowledge of intermittent auscultation. However, reflecting individual clinical experiences, their confidence in their ability to perform intermittent auscultation varied. A lack of opportunity to practice intermittent auscultation, organisational culture, and midwives' preferences have caused student midwives to question their capabilities with this essential clinical skill, leaving some with doubt about their competency close to registration.

A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.

Heart sound segmentation has been shown to improve the performance of artificial intelligence (AI)-based auscultation decision support systems increasingly viewed as a solution to compensate for eroding auscultatory skills and the associated subjectivity. Various segmentation approaches with demonstrated performance can be utilized for this task, but their robustness can suffer in the presence of noise. A noise-robust heart sound segmentation algorithm was developed and its accuracy was tested using two datasets: the CirCor DigiScope Phonocardiogram dataset and an in-house dataset - a heart murmur library collected at the Children's National Hospital (CNH). On the CirCor dataset, our segmentation algorithm marked the boundaries of the primary heart sounds S1 and S2 with an accuracy of 0.28 ms and 0.29 ms, respectively, and correctly identified the actual positive segments with a sensitivity of 97.44%. The algorithm also executed four times faster than a logistic regression hidden semi-Markov model. On the CNH dataset, the algorithm succeeded in 87.4% cases, achieving a 6% increase in segmentation success rate demonstrated by our original Shannon energy-based algorithm. Accurate heart sound segmentation is critical to supporting and accelerating AI research in cardiovascular diseases. The proposed algorithm increases the robustness of heart sound segmentation to noise and viability for clinical use.

Comparison of Cardiac Auscultation Features on Four Different Simulation Mannequins Performed by Pediatric Residents.

Cardiac murmurs are a common problem in pediatric clinical practice. Studies demonstrated low accuracy in detecting and diagnosing various cardiac murmurs at all levels of medical training. So, supplementary training methods started to evolve, including simulation for auscultation skills training. Over the years, mannequins have evolved with different types of technology. Therefore, we decided to compare cardiac auscultation accuracy among high-fidelity mannequins as the primary objective and compare the performance of various postgraduate-level residents as a secondary objective. Pediatric residents at King Abdulaziz University Hospital were given a lecture on the basics of cardiac auscultation and then requested to auscultate four mannequins, namely SimJumior® (Laerdal Medical, Stavanger, Norway), SimBaby™ (Laerdal Medical), Pediatric HAL® (Gaumard Scientific, Miami, FL, USA), and Cardiac Patient Simulator K-Plus (Kyoto Kagaku Co. Ltd., Kyoto, Japan). The accuracies of murmur type, diagnosis, and auscultation time were compared. Results: A total of 56 pediatric residents were enrolled. Median murmur accuracy ranged from 50% to 53% (p-value 0.79), and median diagnosis accuracy ranged from 33% to 36% (p-value 0.77), with a nonsignificant difference between mannequins. Comparing resident levels in all mannequins, median murmur accuracy ranged from 49% to 56% (p-value 0.70), and median diagnosis accuracy ranged from 29% to 41% (p-value 0.09). While the median average auscultation time was between 41 and 50 seconds (p-value 0.34). Auscultation skills can be taught through simulation on any mannequin used in this comparison, not necessarily the cardiac one. For better accuracy, future comparisons might include more advanced cardiac mannequins based on cardiac auscultation expertise (i.e., consultant level). The introduction of an auscultation program from the undergraduate level throughout the training process and monitoring of these skills are mandated.

Assessing feasibility of targeted primary care referrals for patients with clinical suspicion of interstitial lung disease using lung ultrasound: a prospective case finding study. The potential benefits of LUS utilization.

In Primary Health Care (PHC) many interstitial lung disease (ILD) cases may remain at diagnostic delay, due to their challenging presentation and the limited experience of general practitioners (GPs) in recognizing their early symptoms. We have designed a feasibility study to investigate early ILD case-finding competency between PHC and tertiary care. A cross-sectional prospective case-finding study was launched at two private health care centers of Heraklion, Crete, Greece, during nine months (2021-2022). After clinical assessment by GP, PHC attenders, who agreed to participate in the study, were referred to the Respiratory Medicine Department, University Hospital of Heraklion, Crete, underwent Lung Ultrasound (LUS) and those with an overall suspicion for ILDs underwent high resolution computed tomography (HRCT) scan. Descriptive statistics and chi-square tests were used. Multiple Poisson regression analysis was performed to explain positive LUS and HRCT decision with selected variables. One hundred and nine patients out of 183 were finally included (54.1% females; mean age 61, SD: 8.3 years). Thirty-five (32.1%) were current smokers. Overall, two out of ten cases were assessed to need HRCT due to a moderate or high suspicion (19.3%; 95%CI 12.7, 27.4). However, in those who had dyspnea in relation to counterparts, a significantly higher percentage of patients with LUS findings (57.9% vs. 34.0%, p=0.013) was found, as in those who had crackles (100.0% vs. 44.2%, p= 0.005). Detected possible ILD provisional labelling cases were 6, and most importantly, 5 of those cases were considered highly suspicious for further evaluation based on LUS findings. This is a feasibility study exploring potentials by combining data of medical history, basic auscultation skills, as crackles detection, and inexpensive and radiation-free imaging technique, such as LUS. Cases of ILD labeling may be hidden within PHC, sometimes, much before any clinical manifestation.

Design and development of electronic stethoscope for early screening of valvular heart disease prediction

The significant human population of the world is suffering from valvular heart diseases and dies due to the lack of a simple predictive diagnosis system. Identifying the abnormalities in heart sound needs excellent auscultation skills and experience. Thus an electronic stethoscope is designed and developed using different microphonic heads. In this research work, hardware development of an electronic stethoscope interfaced with raspberry pi 4B and software development of the proposed CNN-based deep learning model is carried out. The Bluetooth-enabled electronic stethoscope is used to auscultate the heart sound analyzed with a developed deep learning Convolutional Neural Network-based Efficient Network model. The developed CNN model is designed with classifiers to predict valvular diseases accurately. The analysis result is almost in real-time processed and stored in the Cloud. The design provides a better way of studying PCG signal analysis, which eventually reduces the cost and makes the system compact. The proposed model has been trained through a standard and validated heart sound bank of normal and abnormal diseases and predicts the abnormality with accuracy. The proposed modified Efficient Net - B3 model scored an accuracy of 99.35 ± 0.34% on the test dataset with a sensitivity of 98.84 ± 0.07% and specificity of 98.23 ± 0.52%. The Selenium Web Driver tool with Google Drive API is used to automate the web application PCG signal analysis. Finally, the SQLite database has been used as a back-end server to store the patient record. The system is low-cost and portable, with data remotely accessible and tested with volunteers. The developed system can be used in rural areas where there is a lack of medical facilities exists and can be used to initiate primary screening of valvular diseases.

Design, assembly and validation of a low-cost, high-fidelity simulator for heart exploration.

Heart exploration is an essential clinical competence that requires continuous training and exposure. Low availability and accessibility to patients with heart disease constitutes a barrier to acquiring this competence. Inadequate cardiac auscultation skills in medical students, residents, and graduate physicians have been documented. To develop and validate a low-cost, high-fidelity simulator for heart exploration. A low-cost, high-fidelity heart examination simulator capable of reproducing normal cardiac sounds was designed and developed. Subsequently, the simulator was validated by a group of experts who gave their opinion according to a Likert scale. Ninety-four percent agreed that the simulator motivates the learning of heart exploration, and 92 % considered it to be a realistic model; 91 % considered that the simulator is an attractive tool to reinforce learning and 98 % recommended its further use. The use of the simulator facilitates the acquisition of skills and stimulates learning in the student, which can be attributed to repeated practice, longer exposure time and cognitive interaction.

The effects of simple graphical and mental visualization of lung sounds in teaching lung auscultation during clinical clerkship: A preliminary study.

The study aimed to evaluate visualization-based training's effects on lung auscultation during clinical clerkship (CC) in the Department of Respiratory Medicine on student skills and confidence. The study period was December 2020-November 2021. Overall, 65 students attended a lecture on lung auscultation featuring a simulator (Mr. Lung™). Among them, 35 (visualization group) received additional training wherein they were asked to mentally visualize lung sounds using a graphical visualized lung sounds diagram as an example. All students answered questions on their self-efficacy regarding lung auscultation before and after four weeks of CC. They also took a lung auscultation test with the simulator at the beginning of CC (pre-test) and on the last day of the third week (post-test) (maximum score: 25). We compared the answers in the questionnaire and the test scores between the visualization group and students who only attended the lecture (control group, n = 30). The Wilcoxon signed-rank test and analysis of covariance were used to compare the answers to the questionnaire about confidence in lung auscultation and the scores of the lung auscultation tests before and after the training. Confidence in auscultation of lung sounds significantly increased in both groups (five-point Likert scale, visualization group: pre-questionnaire median 1 [Interquartile range 1] to post-questionnaire 3 [1], p<0.001; control group: 2 [1] to 3 [1], p<0.001) and was significantly higher in the visualization than in the control group. Test scores increased in both groups (visualization group: pre-test 11 [2] to post-test 15 [4], p<0.001; control group: 11 [5] to 14 [4], p<0.001). However, there were no differences between both groups' pre and post-tests scores (p = 0.623). Visualizing lung sounds may increase medical students' confidence in their lung auscultation skills; this may reduce their resistance to lung auscultation and encourage the repeated auscultation necessary to further improve their long-term auscultation abilities.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Physicians' ability to recognize adventitious lung sounds.

Lung auscultation is an important tool for diagnosing respiratory diseases. However, the ability of observers to recognize respiratory sounds varies considerably and depends on the sound. The present study aimed to assess the auscultatory skills of healthcare professionals and medical students. A total of 295 physicians (185 pediatricians, 69 pulmonologists, and 41 physicians of general/internal medicine and subspecialties), 55 residents, and 50 medical students participated in the survey. They listened to five audio-recorded respiratory sounds and described them in free-form answers. The rates of correct answers were 55.2% for fine crackles, 74.5% for coarse crackles, 72.2% for wheezes, 18.75% for squawks, and 11.25% for pleural friction rub. The medical specialty was correlated with the correct answers and both pediatricians and physicians of general/internal medicine and subspecialties recognized fewer sounds compared with respiratory physicians (odds ratio [OR]: 0.37; confidence interval [CI]: 0.22-0.62; p < 0.001 and, OR: 0.47; CI: 0.22-0.99, p = 0.048, respectively). Years of experience were negatively correlated with the number of correct answers (OR: 0.73; CI:0.62-0.84; p = 0.001). Gaps remain in both terminology and recognition of lung sounds among a wide population of Greek physicians. Less experienced physicians perform better on lung auscultation, indicating that continuing education with critical feedback should be offered.

Assessment of Auscultation Skills Among Junior and Senior Healthcare Providers for Pediatric Patients: A Retrospective Study.

BackgroundHeart murmurs are defined as whooshing or swishing sounds, unlike the normal lub-dub sounds detected by physicians while using a stethoscope. They usually develop due to numerous pathologies, with congenital defects accounting for the majority of pediatric murmurs. Few studies have addressed the difference in auscultating skills between senior and junior healthcare providers. Therefore, this study aims to collect local data on this topic as well as identify the gap between experienced and inexperienced providers in their ability to accurately detect heart murmurs.MethodologyThis study utilizes a quantitative retrospective design to collect data from King Abdulaziz Medical City and King Faisal Cardiac Centre, Saudi Arabia, from October 1, 2018, to September 30, 2019. The medical records of 292 pediatric patients, who were 14 years of age or below according to the centers’ aging system, were collected from the Hospital Information System (BESTCare). Subsequently, it was determined whether a senior (R3-R4 residents and above) or a junior (R1-R2 residents and interns) healthcare provider ordered an echocardiogram (ECHO). Finally, using the centers’ imaging system (Xcelera) the exact reason for referral, heart murmurs in the case of this study, was obtained, as well as whether a pathologic cause of a murmur was seen in the ECHO image. By obtaining the aforementioned data, the accuracy of each referral was analyzed using statistical analysis software.ResultsECHO results were categorized into positive and negative outcomes depending on the presence of a structural heart defect, patent foramen ovale (PFO) was considered negative as it causes innocent murmurs. The majority of positive results were atrial septal defects, patent ductus arteriosus, and ventricular septal defects. The majority of negative results were either a structurally normal heart or PFO, which a great number of providers ordered an ECHO for. The Pearson score p-value using the chi-square test was 0.432, leading to the conclusion that junior and senior providers had a similar accuracy of referrals during the study period.ConclusionsJunior healthcare providers display sufficient knowledge of heart murmur auscultation skills similar to senior healthcare providers during the study period. However, because the data only included two local centers with a limited sample and the absence of further local research on this topic, it is necessary to conduct studies of a larger scope on this topic.

Effectiveness of a new interactive web teaching material for improving lung auscultation skills: randomized controlled trial for clinical nurses.

ABSTRACTWe developed a new interactive web-based teaching material to improve lung auscultation skills. Our objective was to investigate the effectiveness of the web-based teaching material on nurses with less than one-year work experience, using a prospective, open-label, stratified block randomized controlled trial. Of the 69 participants, 23, 22, and 24 participants were assigned to the web-based, paper-based, and control (with no intervention) groups, respectively. Using a simulator, a discrimination test on seven lung sounds, such as “normal,” “wheeze,” “rhonchi,” “coarse crackles,” “fine crackles,” “left lung diminish,” and “right lung absent,” was conducted. Next, a post-test was conducted after one-week of training. Answers with formal names were considered “correct”; those with common names, misspellings, and without left and right parts were considered “insufficient”; and wrong answers were considered “incorrect.” The control group showed no significant difference between the pre-test and post-test for any lung sounds. The paper-based group showed significant improvement in performance for “wheeze” (p=0.004) and “coarse crackles” (p=0.035). The web-based group showed a significant improvement in performance for “fine crackles” (p=0.026). The number of correct answers in the post-test was higher in the paper- and web-based groups than the control group (p=0.023). The web-based teaching materials that we had developed effectively improved the ability of new graduate nurses to auscultate lung sounds. Additionally, the results suggest that the combined use of web- and paper-based teaching materials may be more effective since the sounds that each method enhanced their ability to auscultate different lung sounds.

The Effect of a Child Model on Breath-Sounds Examination Skills and Satisfaction on Nursing Students.

One of nursing students’ auscultation critical skills is listening to a child’s breathing sounds. Previously, learning this skill required a SimBaby, which was insufficient, causing nursing students to lack proficiency. Therefore, a CHIld Model (CHIM), an innovation emulating breathing sounds, has been invented based on Gagné’s learning theory to solve this insufficiency. This article reports on the CHIM invention, consisting of hardware, software, and programming, and its effect on nursing students’ breath-sounds examination skills and satisfaction. First, the CHIM was assessed for quality and satisfaction by experts. The results were good in quality and had the highest satisfaction for application in actual use. Second, the CHIM was assessed for auscultation skills and satisfaction among nursing students. Forty-four junior nursing students participated. Next, they were randomly divided into experimental and control groups. Then, both were taught the same about respiratory problems with the SimBaby, except the experimental group had training with the CHIM. After that, both groups’ auscultation skills and satisfaction in the experimental group were examined. Finally, the statistical analysis showed that after the intervention was applied, learning with the CHIM was better than without, with the highest satisfaction level. As intended, the CHIM can help effectively enhance students’ learning and proficiency.

Virtual auscultation course via video chat in times of COVID-19 improves cardiac auscultation skills compared to literature self-study in third-year medical students: a prospective randomized controlled cross-over study

Background: Cardiac auscultation is a core clinical skill taught in medical school. Due to contact restrictions during the SARS-CoV-2 pandemic, interaction with patients was very limited. Therefore, a peer-to-peer virtual case-based auscultation course via video conference was established.Methods: A randomized controlled cross-over study was conducted to evaluate whether participation in a virtual auscultation course could improve heart auscultation skills in 3rd-year medical students. A total of sixty medical students were randomly assigned to either the experimental or control group after informed consent was obtained. Due to no-shows, 55 students participated. Depending on allocation, students attended three ninety-minute courses in intervals of one week in a different order: a virtual case-based auscultation course held via video chat, literature self-study, and an on-site course using a high-fidelity auscultation simulator (SAM II). The study's primary endpoint was the performance of the two groups at the simulator after participating in the virtual auscultation course or literature self-study. To evaluate their auscultation skills, students participated in five assessments using the same six pathologies: stenosis and regurgitation of the aortic and mitral valve, ventricular septal defect, and patent ductus arteriosus. Moreover, participants rated their satisfaction with each course and provided a self-assessment of competence.Results: Compared to literature self-study, participation in the virtual auscultation course led to a significantly improved description of heart murmurs at the auscultation simulator with regard to the presence in systole and diastole, low- and high-pitched sounds, and volume dynamics. There was no significant difference between the groups in diagnostic accuracy and identification of the point of maximal intensity. After the virtual course, students showed higher satisfaction rates and a higher increase in self-assessed competence compared to participants who engaged in literature self-study.Conclusions: For the first time, this study demonstrates that a case-based virtual auscultation course can improve aspects of cardiac auscultation skills on a simulator. This may facilitate the further acquisition of an essential clinical skill, even when contact restrictions will be lifted.

An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone

Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds.

The Effectiveness of Training on Auscultation of Heart with a Simulator of Cardiology in Medical Students

Abstract &#x0D; The purpose of this study was to determine whether the training module with a simulator of cardiology improves auscultation skills in medical students.&#x0D; Methods. Medical students of the third year after completing the module of the cardiovascular system of the discipline “Propaedeutics of internal diseases, passed a two-hour or four-hour training module in clinical auscultation with retesting immediately after the intervention and in the fourth year. The control group consisted of fourth-year medical students who had no intervention.&#x0D; Results. The diagnostic accuracy in two-hour training was 45.9% vs 35.3% in four-hour training p &lt;.001. The use of a cardio simulator significantly increased the accurate detection of mitral regurgitation immediately after training on a simulator (more than 73%) p &lt;.001. The next academic year, regression was observed in the diagnostic accuracy of mitral insufficiency in the intervention group after six months of observation by 4%. The auscultation skills of students at the bedside of real patients did not increase after training on a simulator: the accuracy of diagnosis of the auscultatory picture of the defect was equally low in the intervention group and the control group (35.0% vs 30.8%, p = 0.651).&#x0D; Conclusions. Two-hour training was more effective than four-hour training. After training on cardiac auscultation using a patient’s cardiological simulator, the accuracy rate was low in a situation close to the clinical conditions and a clinic on a real patient.

Transferring Cross-Corpus Knowledge: An Investigation on Data Augmentation for Heart Sound Classification.

Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0 % the sensitivity and by 7.9 % the specificity on average across 6 databases, which is a significant difference on 4 out of these (p < .05 by one-tailed z-test).

The Use of a Virtual Simulator for Practicing the Skills of Auscultation of Sounds of the Lungs of Children in the Formation of Clinical Thinking

Auscultation skills are some of the most important basic methods of patient examination. The educational process of teaching auscultation is complex in itself, and in the context of the pandemic of the new coronavirus infection and the widespread epidemiological limitations, the complexity of learning is multiplied, which requires alternative teaching methods. The Virtual Lung Sound Auscultation Simulator creates a learning platform for auscultation by delivering real pre-recorded cardiopulmonary sounds on the virtual chest of a child.