Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

BACKGROUND

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation.

METHODS

In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081).

FINDINGS

Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators.

INTERPRETATION

Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas.

FUNDING

World Health Organization.

TRANSLATIONS

For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Echocardiographic Screening of Rheumatic Heart Disease: Current Concepts and Challenges

The incidence of acute rheumatic fever (ARF), which most commonly affects children aged 5-15 years after group A Streptococcus (GAS) infection, ranges from 8 to 51 per 100 000 people worldwide. Rheumatic heart disease (RHD), which occurs when patients with ARF are inappropriately treated or not given regular prophylaxis, is the most common cause of non-congenital heart disease in children and young adults in low-income countries. Timely treatment of GAS infection can prevent ARF, and penicillin prophylaxis can prevent recurrence of ARF. Secondary prophylaxis with benzathine penicillin G has been shown to decrease the incidence of RHD and is a key aspect of RHD control. The most important factor determining the prognosis of RHD is the severity of cardiac involvement. Although approximately 70% of patients with carditis in the acute phase of the disease recover without sequelae, carditis is important because it is the only complication of ARF that causes sequelae. One-third of patients with ARF are asymptomatic. Patients with mild symptoms of recurrent ARF and silent RHD will develop severe morbidities within 5-10 years if they do not receive secondary preventive treatments. A new screening program should be established to prevent cardiac morbidities of ARF in moderate- and highrisk populations. In the present study, we examined the applicability of echocardiographic screening programs for RHD. Cite this article as: Topçu S, Uçar T. Echocardiographic screening of rheumatic heart disease: Current concepts and challenges. Turk Arch Pediatr. 2024;59(1):3-12.

Detection and management of latent rheumatic heart disease: a narrative review

Rheumatic heart disease (RHD) is a public health concern in many developing nations around the world. Early detection of latent or subclinical RHD can help in reversing mild lesions, retarding disease progression, reducing morbidity and mortality, and improving the quality of life of patients. Echocardiography is the gold-standard method for screening and confirming latent RHD cases. The rates and determinants of progression of latent RHD cases as assessed by echocardiography have been found to be variable through studies. Even though latent RHD has a slow rate of progression, the rate of progression of its subtype, 'definite' RHD, is substantial. A brief training of nonexpert operators on the use of handheld echocardiography with a simplified protocol is an important strategy to scale up the screening program to detect latent cases. Newer advancements in screening, such as deep-learning digital stethoscopes and telehealth services, have provided an opportunity to expand screening programs even in resource-constrained settings. Newer studies have established the efficacy and safety profile of secondary antibiotic prophylaxis in latent RHD. The concerned authorities in endemic regions of the world should work on improving the availability and accessibility of antibiotic prophylaxis.

A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease

Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide.

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

Machine learning methods for predicting major types of rheumatic heart diseases in children of Southern Punjab, Pakistan

Objective

Rheumatic heart disease (RHD) is a major health problem in the world, particularly in developing countries. This study aimed to predict mitral regurgitation (MR) and mitral stenosis (MS) RHD among children with RHD.

Methodology

Data was collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from March to October 2019. A sample of 561 children aged 4–14 years, who were diagnosed with RHD of either MR or MS, were recruited from the hospital’s outpatient department. The presence of multivariate outliers was detected, and different machine learning methods, including subset logistic regression, subset logistic regression after deletion, stepwise winsorized logistic regression, robust logistic regression, subset deep neural network, and random forest models were compared using the area under receiver operating characteristics (ROC) curve, sensitivity, and specificity. Parsimony was also considered in model selection.

Results

Out of 561 patients in this study, 75.94% had RHD MR and 24.06% had RHD MS. The average age of study participants was 9.19 ± 2.45 years and of them 55.43% were male. Among the male participants, 58.6 and 45.2% had MR and MS, respectively; and among female participants, those were 70.4 and 29.6%, respectively. Subset logistic regression after deletion appeared as competitive with a discrimination power of 90.1% [95% CI 0.818–0.983]. The sensitivity and specificity of this model were 85.1 and 70.6%.

Conclusion

The best predictive model was subset logistic regression after deletion. The predicted method will be used in the decision-making process, which helps early diagnosis of the disease and leads to prevention. The study findings provide the proper guideline for earlier diagnosis of the RHD MR and MS cases among children with RHD in Pakistan.

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.

Real-world evaluation of the Stemoscope electronic tele-auscultation system

BACKGROUND

With the spread of COVID-19, telemedicine has played an important role, but tele-auscultation is still unavailable in most countries. This study introduces and tests a tele-auscultation system (Stemoscope) and compares the concordance of the Stemoscope with the traditional stethoscope in the evaluation of heart murmurs.

METHODS

A total of 57 patients with murmurs were recruited, and echocardiographs were performed. Three cardiologists were asked to correctly categorize heart sounds (both systolic murmur and diastolic murmur) as normal vs. abnormal with both the Stemoscope and a traditional acoustic stethoscope under different conditions. Firstly, we compared the in-person auscultation agreement between Stemoscope and the conventional acoustic stethoscope. Secondly, we compared tele-auscultation (recorded heart sounds) agreement between Stemoscope and acoustic results. Thirdly, we compared both the Stemoscope tele-auscultation results and traditional acoustic stethoscope in-person auscultation results with echocardiography. Finally, ten other cardiologists were asked to complete a qualitative questionnaire to assess their experience using the Stemoscope.

RESULTS

For murmurs detection, the in-person auscultation agreement between Stemoscope and the acoustic stethoscope was 91% (p = 0.67). The agreement between Stemoscope tele-auscultation and the acoustic stethoscope in-person auscultation was 90% (p = 0.32). When using the echocardiographic findings as the reference, the agreement between Stemoscope (tele-auscultation) and the acoustic stethoscope (in-person auscultation) was 89% vs. 86% (p = 1.00). The system evaluated by ten cardiologists is considered easy to use, and most of them would consider using it in a telemedical setting.

CONCLUSION

In-person auscultation and tele-auscultation by the Stemoscope are in good agreement with manual acoustic auscultation. The Stemoscope is a helpful heart murmur screening tool at a distance and can be used in telemedicine.

Recent Advances in the Rheumatic Fever and Rheumatic Heart Disease Continuum

Nearly a century after rheumatic fever (RF) and rheumatic heart disease (RHD) was eradicated from the developed world, the disease remains endemic in many low- and middle-income countries (LMICs), with grim health and socioeconomic impacts. The neglect of RHD which persisted for a semi-centennial was further driven by competing infectious diseases, particularly the human immunodeficiency virus (HIV) pandemic. However, over the last two-decades, slowly at first but with building momentum, there has been a resurgence of interest in RF/RHD. In this narrative review, we present the advances that have been made in the RF/RHD continuum over the past two decades since the re-awakening of interest, with a more concise focus on the last decade’s achievements. Such primary advances include understanding the genetic predisposition to RHD, group A Streptococcus (GAS) vaccine development, and improved diagnostic strategies for GAS pharyngitis. Echocardiographic screening for RHD has been a major advance which has unearthed the prevailing high burden of RHD and the recent demonstration of benefit of secondary antibiotic prophylaxis on halting progression of latent RHD is a major step forward. Multiple befitting advances in tertiary management of RHD have also been realized. Finally, we summarize the research gaps and provide illumination on profitable future directions towards global eradication of RHD.