The first 200 years of cardiac auscultation and future perspectives

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.

Implementation of stethoscope disinfection: an observational study on nursing staff practice and knowledge

Background

Healthcare-associated infections cause high mortality and morbidity, and lack of stethoscope disinfection is one of the reasons for healthcare-associated infections. Nurses who frequently use stethoscopes in the clinic do not disinfect stethoscopes at high rates. This study aimed to identify the frequency of stethoscope disinfection by nurses and their knowledge about the same.

Methods

This was a mixed-methods observational study. The quantitative part of the study included 202 nurses, the qualitative part included 12. Two researchers who made observations during stethoscope use recorded the procedures the nurses performed on the "Observation Form". Semi-structured in-depth interviews were conducted based on phenomenological methods.

Results

23.7% of the nurses disinfected their stethoscopes before contact with patients, 11.8% after contact with patients and 6.4% before and after contact with patients. The nurses used a stethoscope on an average of 7.42 patients without disinfecting it. In the qualitative interview, some nurses stated that they did not have information about the disinfectants to be used for stethoscopes and their effectiveness. Some of the participants in the present study stated that they did not receive training on stethoscope disinfection and that they did not know that there were guidelines about it.

Conclusion

Since there were deficiencies in the implementation of stethoscope disinfection as well as knowledge, the transfer of knowledge in this context must receive more attention in education and training.

Evaluation of arteriovenous fistula for hemodialysis with a new generation digital stethoscope: a pilot study

BACKGROUND AND AIMS

The management of complications of arteriovenous fistula (AVF) for hemodialysis, principally stenosis, remains a major challenge for clinicians with a substantial impact on health resources. Stenosis not infrequently preludes to thrombotic events with the loss of AVF functionality. A functioning AVF, when listened by a stethoscope, has a continuous systolic-diastolic low-frequency murmur, while with stenosis, the frequency of the murmur increases and the duration of diastolic component decreases, disappearing in severe stenosis. These evidences are strictly subjective and dependent from operator skill and experience. New generation digital stethoscopes are able to record sound and subsequently dedicated software allows to extract quantitative variables that characterize the sound in an absolutely objective and repeatable way. The aim of our study was to analyze with an appropriate software sounds from AVFs taken by a commercial digital stethoscope and to investigate the potentiality to develop an objective way to detect stenosis.

METHODS

Between September 2022 and January 2023, 64 chronic hemodialysis (HD) patients were screened by two blinded experienced examiners for recognized criteria for stenosis by Doppler ultrasound (DUS) and, consequently, the sound coming from the AVFs using a 3 M™ Littmann® CORE Digital Stethoscope 8570 in standardized sites was recorded. The sound waves were transformed into quantitative variables (amplitude and frequency) using a sound analysis software. The practical usefulness of the core digital stethoscope for a quick identification of an AVF stenosis was further evaluated through a pragmatic trial. Eight young nephrologist trainees underwent a simple auscultatory training consisting of two sessions of sound auscultation focusing two times on a "normal" AVF sound by placing the digital stethoscope on a convenience site of a functional AVF.

RESULTS

In 48 patients eligible, all sound components displayed, alone, a remarkable diagnostic capacity. More in detail, the AUC of the average power was 0.872 [95% CI 0.729-0.951], while that of the mean normalized frequency was 0.822 [95% 0.656-0.930]. From a total of 32 auscultations (eight different block sequences, each one comprising four auscultations), the young clinicians were able to identify the correct sound (stenosis/normal AVF) in 25 cases, corresponding to an overall accuracy of 78.12% (95% CI 60.03-90.72%).

CONCLUSIONS

The analysis of sound waves by a digital stethoscope permitted us to distinguish between stenotic and no stenotic AVFs. The standardization of this technique and the introducing of data in a deep learning algorithm could allow an objective and fast method for a frequent monitoring of AVF.

A randomized, controlled trial of an innovative, multimedia instructional program for acquiring auditory skill in identifying pediatric heart murmurs

Objective

To create a brief, acceptable, innovative method for self-paced learning to enhance recognition of pediatric heart murmurs by medical students, and to demonstrate this method's effectiveness in a randomized, controlled trial.

Materials and methods

A curriculum of six 10-min online learning modules was designed to enable deliberate practice of pediatric cardiac auscultation, using recordings of patients' heart murmurs. Principles of andragogy and multimedia learning were applied to optimize acquisition of this skill. A pretest and posttest, given 4 weeks apart, were created using additional recordings and administered to 87 3rd-year medical students during their pediatric clerkship. They were randomized to have access to the modules after the pretest or after the posttest, and asked to use at least the first 2 of the modules.

Results

47 subjects comprised the Intervention group, and 40 subjects the Control group. On our primary outcome, distinguishing innocent from pathological with at least moderate confidence, the posttest scores were significantly higher for the Intervention group (60.5%) than for the Control group (20.0%). For our secondary outcomes, the 2 groups also differed significantly in the ability to distinguish innocent from pathological murmurs, and in identifying the actual diagnosis. On all 3 outcomes, those Intervention group subjects who accessed 4-6 modules scored higher than those who accessed 0-3 modules, who in turn scored higher than the Control group.

Summary

Applying current principles of adult learning, we have created a teaching program for medical students to learn to recognize common pediatric murmurs. Its effectiveness was demonstrated in a randomized, controlled trial. The program results in a meaningful gain in this skill from 1 h of self-paced training with high acceptance to learners.

Physicians' perceptions of the daily use of a handheld ultrasound device in the labor room

OBJECTIVE

The objective of our study was to describe the perception of physicians who use a handheld ultrasound (US) device in an intensive perinatal care unit.

METHODS

We conducted a prospective observational study in the labor ward of an intensive perinatal care unit between November 2021 and May 2022. Obstetrics & Gynecology residents in rotation in our department during this time were recruited as participants in this study. All the participants were provided with a handheld US device Vscan Air™ (GE Healthcare, Zipf, Austria) to use during their normal days and nights practice in labor ward. At the end of their 6 months rotation, participants completed an anonymous surveys about their perceptions of the handheld US device. The survey included questions about the ease of use in clinical situations, the amount of time of initial diagnosis, performances of the device, feasibility to use, and patient's satisfaction with the use of the device.

RESULTS

6 residents in their last year of residency were included. All the participants were satisfied with the device and would like to use it in their future practice. They all agreed that the probe was easy to handle and that the mobile application was easy to use. Image quality was always considered good by the participants and 5/6 of them declared that the handheld US device was always sufficient and did not require any confirmation with a conventional US machine. 5/6 of the participants considered that the handheld US device allowed them to gain time for clinical decision but half of them did not estimate that the use of the handheld US device improved their ability to make a clinical diagnosis.

CONCLUSION

Our study suggests that the Vscan Air™ is easy to use, with a good quality image and reduces the amount of time to make a clinical diagnosis. Handheld US device could be useful in the daily practice in maternity hospital.

Utilization of Simulation to Teach Cardiac Auscultation: A Systematic 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.

StethAid: A Digital Auscultation Platform for Pediatrics

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

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.

Design and Fabrication of an Integrated Hollow Concave Cilium MEMS Cardiac Sound Sensor

In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies. By rationally designing the acoustic encapsulation, the loss of heart acoustic distortion and weak cardiac murmurs is reduced. As demonstrated by experimentation, the proposed hollow MEMS structure cardiac sound sensor has a sensitivity of up to -206.9 dB at 200 Hz, showing 6.5 dB and 170 Hz increases in the sensitivity and operating bandwidth, respectively, in contrast to the planar ciliated MEMS sensor. The SNR of the sensor is 26.471 dB, showing good detectability for cardiac sounds.

The Point-of-Care Ultrasound (POCUS) by the Handheld Ultrasound Devices (HUDs) in the COVID-19 Scenario: a Review of the Literature

The use of portable ultrasound (US) devices is increasing, due to its accessibility, versatility, non-invasiveness, and its significant support in the patient management, extending the traditional physical examination through the POCUS (point-of-care ultrasound). The pocket-size or handheld ultrasound devices (HUDs) can easily perform focused exams, not aiming to substitute for the high-end US systems (gold standard), since the HUDs usually have more limited functions. The HUDs are promising tools for the diagnosis, prognosis, and monitoring of the COVID-19 infection and its related disorders. In conclusion, the routine use of HUDs may ameliorate the management of COVID-19 pandemic, according to the guidelines for the POCUS approach and the procedures for the protection of the patients and the professionals.

Artificial Intelligence-Based Stethoscope for the Diagnosis of Aortic Stenosis

BACKGROUND

The diagnostic accuracy of the stethoscope is limited and highly dependent on clinical expertise. Our purpose was to develop an electronic stethoscope, based on artificial intelligence (AI) and infrasound, for the diagnosis of aortic stenosis (AS).

METHODS

We used an electronic stethoscope (VoqX; Sanolla, Nesher, Israel) with subsonic capabilities and acoustic range of 3-2000 Hz. The study had 2 stages. In the first stage, using the VoqX, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS and 50 without valvular disease. An AI-based supervised learning model was applied to the auscultation data from the first 100 patients used for training, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). In the second stage, conducted at a different medical center, we tested the device on 106 additional patients referred for echocardiography, which included patients with other valvular diseases.

RESULTS

Using data collected at the aortic and pulmonic auscultation points from the derivation group, the AI-based algorithm identified moderate or severe AS with 86% sensitivity and 100% specificity. When applied to the validation group, the sensitivity was 84% and specificity 92%; and in the additional testing group, 90% and 84%, respectively. The sensitivity was 55% for mild, 76% for moderate, and 93% for severe AS.

CONCLUSION

Our initial findings show that an AI-based stethoscope with infrasound capabilities can accurately diagnose AS. AI-based electronic auscultation is a promising new tool for automatic screening and diagnosis of valvular heart disease.

Design and Realization of MEMS Heart Sound Sensor with Concave, Racket-Shaped Cilium

The biomedical acoustic signal plays an important role in clinical non-invasive diagnosis. In view of the deficiencies in early diagnosis of cardiovascular diseases, acoustic properties of S₁ and S₂ heart sounds are utilized. In this paper, we propose an integrated concave cilium MEMS heart sound sensor. The concave structure enlarges the area for receiving sound waves to improve the low-frequency sensitivity, and realizes the low-frequency and high-sensitivity characteristics of an MEMS heart sound sensor by adopting a reasonable acoustic package design, reducing the loss of heart sound distortion and faint heart murmurs, and improving the auscultation effect. Finally, experimental results show that the integrated concave ciliated MEMS heart sound sensor’s sensitivity reaches −180.6 dB@500 Hz, as compared with the traditional bionic ciliated MEMS heart sound sensor; the sensitivity is 8.9 dB higher. The sensor has a signal-to-noise ratio of 27.05 dB, and has good heart sound detection ability, improving the accuracy of clinical detection methods.

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.

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.

Connecting the dots: Bridging virtual to in-person physical assessment

When restrictions imposed by COVID-19 prevented prelicensure nursing students from practicing skills in the simulation center, the faculty designed a plan to bridge the gap from virtual to in-person skill performance for physical assessments. The faculty anticipated an inadequacy of head-to-toe assessment skills related to the lack of face-to-face clinical. Therefore, the faculty used the Plan-Do-Study-Act model (Agency for Healthcare Research and Quality, 2020) for quality improvement to address the skill performance issue. The plan used colored adhesive "garage sale" dots to identify anatomical landmarks to help students with correct stethoscope placement during their first in-person simulation after a virtual semester. Pre- and post-tests were administered to assess confidence in assessment skills. Using this method on standardized patients in simulation, nursing students reported increased confidence in stethoscope placement. Students of all healthcare disciplines could benefit from the economical dotting process to learn correct stethoscope placement.

Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.

Rheumatic Heart Disease Screening Based on Phonocardiogram

Rheumatic heart disease (RHD) is one of the most common causes of cardiovascular complications in developing countries. It is a heart valve disease that typically affects children. Impaired heart valves stop functioning properly, resulting in a turbulent blood flow within the heart known as a murmur. This murmur can be detected by cardiac auscultation. However, the specificity and sensitivity of manual auscultation were reported to be low. The other alternative is echocardiography, which is costly and requires a highly qualified physician. Given the disease's current high prevalence rate (the latest reported rate in the study area (Ethiopia) was 5.65%), there is a pressing need for early detection of the disease through mass screening programs. This paper proposes an automated RHD screening approach using machine learning that can be used by non-medically trained persons outside of a clinical setting. Heart sound data was collected from 124 persons with RHD (PwRHD) and 46 healthy controls (HC) in Ethiopia with an additional 81 HC records from an open-access dataset. Thirty-one distinct features were extracted to correctly represent RHD. A support vector machine (SVM) classifier was evaluated using two nested cross-validation approaches to quantitatively assess the generalization of the system to previously unseen subjects. For regular nested 10-fold cross-validation, an f1-score of 96.0 ± 0.9%, recall 95.8 ± 1.5%, precision 96.2 ± 0.6% and a specificity of 96.0 ± 0.6% were achieved. In the imbalanced nested cross-validation at a prevalence rate of 5%, it achieved an f1-score of 72.2 ± 0.8%, recall 92.3 ± 0.4%, precision 59.2 ± 3.6%, and a specificity of 94.8 ± 0.6%. In screening tasks where the prevalence of the disease is small, recall is more important than precision. The findings are encouraging, and the proposed screening tool can be inexpensive, easy to deploy, and has an excellent detection rate. As a result, it has the potential for mass screening and early detection of RHD in developing countries.

Stethoscope - An essential diagnostic tool or a relic of the past?

Rene Laennec came up with the idea of a stethoscope in 1816 to avoid the embarrassment of performing immediate auscultation on women. Soon many doctors around the world started using this tool because of its increased accuracy and ease of use. Stethoscopes hold great significance in the medical community. However, is the importance placed on stethoscopes justified today? We now have devices like portable ultrasound machines that make it much easier to visualize the body. These devices offset their higher initial cost by reducing downstream costs due to their greater accuracy and their capability of detecting diseases at an earlier stage. Also, because of the COVID-19 pandemic, new ways are being investigated to reduce the transmission of diseases. Stethoscopes being a possible vector for infectious agents coupled with the advent of newer devices that can visualize the body with greater accuracy put into question the continued use of stethoscopes today. With that said, the use of stethoscopes to diagnose diseases is still crucial in places where buying these new devices is not yet possible. The stethoscope is a great symbol of medicine, but its use needs to be in line with what is best for the patient.

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

Case of Supracristal Ventricular Septal Defect and Aortic Regurgitation Detected by Cardiac Auscultation but Missed by Diagnostic Imaging

Imaging technology has diminished the reliance on cardiac auscultation as a definitive diagnostic tool. However, it retains relevance in its immediacy, minimal preparation, and power source independence. We present a case of clinically detected continuous murmur raising specific diagnostic possibilities not accounted for advanced imaging. Further testing revealed a large supracristal ventricular septal defect (VSD) and aortic regurgitation (AR), allowing the surgeon to anticipate combined septal and valvular surgery. This report highlights the value of cardiac auscultation as a guide and validation for imaging. The absence of lesions on imaging is not proof of lesion absence.

Do basic auscultation skills need to be resuscitated? A new strategy for improving competency among nursing students

BACKGROUND

Auscultation of heart and lung sounds is a foundational competency for Registered Nurses (RNs). Precise and timely assessments are important for the early detection and recognition of the deteriorating patient. Studies have shown that improved teaching methods that incorporate emerging technologies and address different learning styles are needed to improve competency in auscultation.

METHOD

Undergraduate nursing students (n = 127) were randomized into treatment and control groups. The control group received the usual preparation in auscultation learning strategies. The treatment group received the usual training plus three auscultation learning sessions that were each 2 h in length (cardiac, pulmonary and mixed sounds).

RESULTS

The virtual auscultation teaching strategy had a significant impact on undergraduate nursing student's competency in recognizing heart murmurs. The treatment group also had increased scores compared to the control group increased scores in distinguishing normal versus abnormal heart and lung sounds, identification of crackles and diminished breath sounds.

CONCLUSION

Virtual auscultation as a teaching strategy was shown to have a positive impact on undergraduate student nurse competence in accurately identifying heart and lung sounds.