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PLOS Digital Health Sep 2023Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and... (Review)
Review
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.
PubMed: 37695769
DOI: 10.1371/journal.pdig.0000324 -
The Journal of Physician Assistant... Dec 2023Current physician assistant (PA) learners have a clear preference for interactive learning that is vibrantly present in new media technologies. At present, there is a...
INTRODUCTION
Current physician assistant (PA) learners have a clear preference for interactive learning that is vibrantly present in new media technologies. At present, there is a paucity of research regarding use or acceptability of gamification in PA education. The purpose of this study was to examine PA students' experience with, attitudes toward, and outcomes of a gamified cardiac auscultation curriculum.
METHODS
Faculty at one institution designed an interactive Mobile App Cardiac Auscultation Curriculum (MACAC). The MACAC incorporates independent and group learning using the Littmann Learning mobile app. Author-created surveys as well as knowledge and auscultation assessment tools were delivered to all students.
RESULTS
Most of the students recommended the use of the app for future cohorts and reported confidence to accurately identify normal and abnormal heart sounds. Knowledge and auscultation assessment scores demonstrated proficiency in identification of normal and abnormal heart sounds.
DISCUSSION
Gamification research is important because blended learning that incorporates new media technologies with traditional approaches can help overcome the limitations of passive learning environments. This study provides evidence that the use of a mobile app can be an effective and innovative method to teach cardiac auscultation to the 21st century PA learners.
Topics: Humans; Heart Auscultation; Gamification; Clinical Competence; Physician Assistants; Students
PubMed: 37678814
DOI: 10.1097/JPA.0000000000000535 -
Canadian Journal of Kidney Health and... 2023Lung ultrasound is a noninvasive bedside technique that can accurately assess pulmonary congestion by evaluating extravascular lung water. This technique is expanding... (Review)
Review
PURPOSE OF REVIEW
Lung ultrasound is a noninvasive bedside technique that can accurately assess pulmonary congestion by evaluating extravascular lung water. This technique is expanding and is easily available. Our primary outcome was to compare the efficacy of volume status assessment by lung ultrasound with clinical evaluation, echocardiography, bioimpedance, or biomarkers. The secondary outcomes were all-cause mortality and cardiovascular events.
SOURCES OF INFORMATION
We conducted a MEDLINE literature search for observational and randomized studies with lung ultrasound in patients on maintenance dialysis.
METHODS
From a total of 2363 articles, we included 28 studies (25 observational and 3 randomized). The correlation coefficients were pooled for each variable of interest using the generic inverse variance method with a random effects model. Among the clinical parameters, New York Heart Association Functional Classification of Heart Failure status and lung auscultation showed the highest correlation with the number of B-lines on ultrasound, with a pooled correlation coefficient of .57 and .36, respectively. Among echocardiographic parameters, left ventricular ejection fraction and inferior vena cava index had the strongest correlation with the number of B-lines, with a pooled coefficient of .35 and .31, respectively. Three randomized studies compared a lung ultrasound-guided approach with standard of care on hard clinical endpoints. Although patients in the lung ultrasound group achieved better decongestion and blood pressure control, there was no difference between the 2 management strategies with respect to death from any cause or major adverse cardiovascular events.
KEY FINDINGS
Lung ultrasound may be considered for the identification of patients with subclinical volume overload. Trials did not show differences in clinically important outcomes. The number of studies was small and many were of suboptimal quality.
LIMITATIONS
The included studies were heterogeneous and of relatively limited quality.
PubMed: 38148768
DOI: 10.1177/20543581231217853 -
Diagnosis (Berlin, Germany) Aug 2023
Topics: Humans; Heart Sounds; Auscultation; Lung
PubMed: 36869892
DOI: 10.1515/dx-2023-0011 -
Florence Nightingale Journal of Nursing Oct 2023This study aimed to evaluate the agreement between epigastric auscultation and pH measurement in the confirmation of nasoenteral tube placement.
AIM
This study aimed to evaluate the agreement between epigastric auscultation and pH measurement in the confirmation of nasoenteral tube placement.
METHOD
A cross-sectional study carried out in a medium-sized private hospital in the interior of the state of São Paulo. Forty-nine patients who were submitted to ninety insertion procedures and confirmation of tube placement. aimed at evaluating the agreement of clinical methods used by nurses to confirm the positioning of a nasoenteral tube inserted blindly at the bedside, by measuring the parameters of sensitivity, specificity, positive predictive value, and negative predictive value.
RESULTS
The epigastric auscultation was the method that presented the highest sensitivity (100.0%), but lower specificity (2.0%). The measurement of the pH presented lower sensitivity (63.0%) than the auscultation, however, higher specificity (58%). Moreover, the positive predictive value of the pH measurement was 55% and the negative predictive value was 66%. There was no agreement between the epigastric auscultation and the pH measurement with the radiography.
CONCLUSION
The pH measurement did not allow for distinguishing between gastric and enteric positioning, due to the similarity of the esophageal and pulmonary pH with the pH of the intestine. Furthermore, external factors such as the use of medication and reduced fasting time may interfere with the pH value.
PubMed: 37823827
DOI: 10.5152/FNJN.2023.22240 -
Frontiers in Digital Health 2023Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and... (Review)
Review
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.
PubMed: 37767523
DOI: 10.3389/fdgth.2023.1196079 -
International Journal of Medical... Oct 2023Telehealth was rapidly adopted in primary care during COVID-19. However, there is a lack of research assessing how translatable in-person consultations are to telehealth.
BACKGROUND
Telehealth was rapidly adopted in primary care during COVID-19. However, there is a lack of research assessing how translatable in-person consultations are to telehealth.
OBJECTIVE
To examine insights from in-person GP-Patient consultations for patients with chronic conditions, including 1/frequency, duration, conditions of physical examinations, and when they occur during consultations, 2/types of physical artefacts used, 3/clinical tasks performed, and 4/translatability of clinical tasks to telehealth.
METHODS
Eligible consultations were extracted from a dataset archive named HaRI, which contains 281 in-person GP consultations in de-identified transcript and video format. 38 consultations were included for analysis meeting eligibility criteria in this study. A multi-method approach (using content analysis, visualisation, video and time analysis) was applied to eligible consultations, extracting clinical tasks that involve physical interactions. Finally, an evidence-based scoring system was used on each clinical task, determining the likelihood of whether each task could be translated into telehealth.
RESULTS
Nine chronic conditions were observed across 38 GP-Patient consultations, predominately diabetes (39 %, 15/38). Out of these 38 consultations, 76 % (29/38) featured physical examinations, where 68 % (26/38) were initiated by GPs (e.g., auscultation), and 26 % (10/38) were initiated by patients (e.g., self-palpation). The average percentage of time spent on physical examination(s) during consultations is low (13.6 %, SD = 9.4 %). A total of 24 clinical tasks were observed across these 38 consultations. Out of these 24 tasks, 92 % (22/24) were supported by physical artefacts. The average score of a task being translatable to Telehealth is 7/10 (where Score 1 = Not amenable to being replicated over telehealth at this stage, scoring 10 = Easily translatable over telehealth with almost no additional equipment being required).
CONCLUSION
All tasks observed across chronic condition management visits were deemed translatable/potentially translatable to telehealth. However, physical interactions between GPs and patients are still essential. Future research in telehealth should focus on examining ways to support physical examination, reduce uncertainty, promote safety netting, and facilitate patients' safety at home with effective technology and support.
Topics: Humans; COVID-19; Telemedicine; Referral and Consultation; Primary Health Care; Chronic Disease
PubMed: 37619394
DOI: 10.1016/j.ijmedinf.2023.105197 -
IEEE Journal of Biomedical and Health... Sep 2023This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077...
This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.
Topics: Humans; Female; Male; Blood Pressure; Photoplethysmography; Blood Pressure Determination; Hypertension; Pulse Wave Analysis
PubMed: 37204948
DOI: 10.1109/JBHI.2023.3278168 -
Sensors (Basel, Switzerland) Dec 2023Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment....
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.
Topics: Humans; Artificial Intelligence; Auscultation; Electrocardiography; Neural Networks, Computer; Ventricular Dysfunction
PubMed: 38139680
DOI: 10.3390/s23249834 -
Bioengineering (Basel, Switzerland) Jun 2024Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early...
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.
PubMed: 38927822
DOI: 10.3390/bioengineering11060586