Detection Of Rheumatic Heart Disease Research Articles

Automated Detection of Rheumatic Heart Disease via Unsegmented Heart Sound Analysis

Rheumatic Heart Disease (RHD) remains a significant cause of morbidity and mortality in many parts of the world, particularly in low-resource settings. Early detection and intervention are critical to mitigating its progression and reducing associated complications. This paper presents an automated approach for the detection of RHD utilizing unsegmented heart sound analysis through deep learning techniques. By leveraging Spectro-temporal representations of raw heart sound signals, our proposed method aims to capture subtle yet discriminative patterns indicative of RHD pathology. The deep learning model is trained on a dataset comprising a diverse range of heart sounds, including those from both RHD-positive and RHD-negative individuals. Through rigorous evaluation of unseen data, our approach demonstrates promising performance in accurately distinguishing between RHD and healthy heart sounds. Furthermore, the proposed methodology offers the potential for scalable and cost-effective screening in resource-limited settings, thereby facilitating early identification and management of RHD cases. This work contributes to the advancement of computer-aided diagnostic tools for cardiovascular diseases, with implications for improving healthcare outcomes and reducing the burden of RHD globally. Key Words: Rheumatic Heart Disease, Heart Sound Analysis, Deep Learning, Automated Detection, Unsegmented Signals, Spectro-Temporal Representation, Cardiovascular Disease.

Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation.

Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.

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, ʻdefiniteʼ 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.

Morpho-mechanistic screening criteria for the echocardiographic detection of rheumatic heart disease

IntroductionScreening echocardiography, guided by the current World Heart Federation (WHF) criteria, has important limitations that impede the establishment of large-scale rheumatic heart disease (RHD) control programmes in endemic regions. The...

Abbreviated Echocardiographic Screening for Rheumatic Heart Disease by Nonexperts with and without Offsite Expert Review: A Diagnostic Accuracy Study

Early detection of rheumatic heart disease (RHD) through echocardiographic screening can facilitate early access to effective treatment, which reduces the risk for progression. Accurate, feasible approaches to echocardiographic screening that can be incorporated into routine health services are needed. The authors hypothesized that offsite expert review could improve the diagnostic accuracy of nonexpert-obtained echocardiographic images. This prospective cross-sectional study was performed to evaluate the diagnostic accuracy of health worker-conducted single parasternal long-axis view with a sweep of the heart using hand-carried ultrasound for the detection of RHD in high-risk populations in Timor-Leste and Australia. In the primary analysis, the presence of any mitral or aortic regurgitation met the criteria for a positive screening result. Sensitivity and specificity were calculated for a screen-and-refer approach based on nonexpert practitioner assessment (approach 1) and for an approach using offsite expert review of nonexpert practitioner-obtained images to decide onward referral (approach 2). Each participant had a reference test performed by an expert echocardiographer on the same day as the index test. Diagnosis of RHD was determined by a panel of three experts, using 2012 World Heart Federation criteria. The prevalence of borderline or definite RHD among 3,329 participants was 4.0% (95% CI, 3.4%-4.7%). The sensitivity of approach 1 for borderline or definite RHD was 86.5% (95% CI, 79.5%-91.8%), and the specificity was 61.4% (95% CI, 59.7%-63.1%). Approach 2 achieved similar sensitivity (88.4%; 95% CI, 81.5%-93.3%) and improved specificity (77.1%; 95% CI, 75.6%-78.6%). Nonexpert practitioner-obtained single parasternal long-axis view with a sweep of the heart images, reviewed by an offsite expert, can detect borderline and definite RHD on screening with reasonable sensitivity and specificity. Brief training of nonexpert practitioners with ongoing support could be used as an effective strategy for scaling up echocardiographic screening for RHD in high-risk settings.

Abstract 12284: Utilizing Automated Machine Learning for Rheumatic Heart Disease Detection in Doppler Echocardiography

Introduction: Rheumatic heart disease (RHD) is the number one cause globally of morbidity and mortality from heart disease in children and young adults. The mitral regurgitation (MR) jet length on color Doppler echocardiography is an important index for diagnosis, but its measurement and interpretation vary. Objective: Develop an automatic machine learning approach to identify and measure the MR jet length on color Doppler for RHD detection. Methods: We used 316 echocardiograms from 95 children (mean age 12±2 years; range 5 to 17 years) with DICOM color Doppler images of the mitral valve taken from parasternal long axis (PLAX) and apical 4 chamber (AP4) views. All echocardiograms were independently reviewed by an adjudication panel consisting of four expert pediatric cardiologists to determine maximum MR jet length and diagnosis (RHD or normal). Among 95 cases, 29 were normal and 66 had RHD. Our automated method included. (1) Selection of frames during ventricular systole using a convolutional neural network architecture. (2) Localization of left atrium using convolutional neural networks with LinkNet structure. (3) Measurement of MR jet length by image color analysis. (4) Detection of RHD by applying a generalized regression model based on the maximum MR jet length measured and maximizing the balanced accuracy using cross validation. Results: Machine learning selected the correct systolic frame with an average accuracy of 0.95 (sensitivity 97%/specificity 93%) and 0.94 (sensitivity 94%/specificity 94%) for the AP4 and PLAX view, respectively. It localized the atrium with an average Dice coefficient of 0.89 and 0.9 for the AP4 and PLAX view, respectively. We estimated the MR jet length with an average absolute error of 0.33±0.4 cm (p-value = 0.15 compared to manual measurements). Our deep learning approach performed similar to or better than previously published manual methods for categorization of RHD positive vs negative. The accuracy of RHD detection was 0.84 (sensitivity 86%/specificity 79%). Conclusions: Our automatic method has the potential to reliably detect RHD as accurately as expert cardiologists. This innovative approach holds promise to scale echocardiography screening for RHD and expand prophylaxis to prevent progression of RHD globally.

Survival of Rheumatic Heart Disease in Indonesian Children.

Rheumatic heart disease (RHD) remains a preventable cause of cardiovascular deaths in children in low- and middle-income countries. However, long-term outcome data of children with RHD is still lacking in Indonesia. To investigate the long-term outcomes of RHD, particularly the survival rates and the predictors. A retrospective cohort study was done in children aged less than 18 years old admitted with RHD at a tertiary hospital (Dr. Sardjito Hospital, Indonesia) from 2011-2021. Survival time was estimated from the date of first diagnosis of RHD to the survival endpoint (date of mortality or censoring). Kaplan-Meier curves, log-rank test and Cox-regression analysis were used for survival analysis and the predictors. A total of 155 patients admitted with RHD during the study period. Of these, 14 (9.0%) deaths were reported as RHD related mortality with the mean age ± standard deviation of 11.9 ± 2.9 years. Median follow up period was 16 months. The survival rate at 1, 4, and 8 years were 93%, 86.1% and 60%, respectively. Survival was lower in patients with severe valve lesions and NYHA functional class III-IV at the time of diagnosis. Multivariate Cox-regression analysis showed the significant predictors for mortality were poor adherence to penicillin and congestive heart failure, HR 12.7 (95%CI 1.4-113.5) and 18.6 (95%CI 1.8-185.0) respectively. Approximately, only six in 10 children are able to survive at eight years after diagnosis. Poor adherence to penicillin and congestive heart failure were predictors for death. This study emphasizes the needs to improve the adherence of children with RHD and early detection of subclinical carditis in children.

A Multi-Layer Acoustic Neural Network-Based Intelligent Early Diagnosis System for Rheumatic Heart Disease

Rheumatic Heart Disease (RHD) is a disorder of heart caused by streptococcal throat infection followed by the organ damage, irreversible valve damage and heart failure. Acute Rheumatic Fever (ARF) is a precursor to the disease. Sometimes, RHD can occur without any signs or symptoms, and if there are any symptoms, they occur with the infection in the heart valves and fever. Due to these issues, respiratory problems occur with chest pain and tremors. Additionally, the symptoms include faint, heart murmurs, stroke and unexpected collapse. The techniques available try to detect the RHD as early as possible. Although the recent medical health care department uses crucial techniques, they are not accurate in terms of symptom classification, precision and prediction. On the scope, we are developing Multi-Layered Acoustic Neural (MLAN) Networks to detect the RHD symptoms using heart beat sound and Electrocardiogram (ECG) measurements. In this proposed MLAN system, the novel techniques such as multi-attribute acoustic data sampling model, heart sound sampling procedures, ECG data sampling model, RHD Recurrent Convolutional Network (RRCN) and Acoustic Support Vector Machine (ASVM) are used for increasing the accuracy. In the implementation section, the proposed model has been compared to the Long Short-Term Memory-based Cardio (LSTC) data analysis model, Cardio-Net and Video-Based Deep Learning (VBDL) techniques. In this comparison, the proposed system has 10%–17% higher accuracy in RHD detection than existing techniques.

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.

Standard echocardiography versus handheld echocardiography for the detection of subclinical rheumatic heart disease: a systematic review and meta-analysis of diagnostic accuracy

ObjectiveTo summarise the accuracy of handheld echocardiography (HAND) which, if shown to be sufficiently similar to that of standard echocardiography (STAND), could usher in a new age of rheumatic heart...

Prevalence of rheumatic heart disease in North-Central Nigeria: a school-based cross-sectional pilot study.

To present epidemiological data on rheumatic heart disease (RHD), the most common acquired heart disease in children and young adults in low- and middle-income countries, for North-Central Nigeria. In this pilot study, we conducted clinical and echocardiography screening on a cross section of randomly selected secondary schoolchildren in Jos, North-Central Nigeria, from March to September 2016. For outcome classification into borderline or definite RHD, we performed a confirmatory echocardiography using the World Heart Federation criteria for those suspected to have RHD from the screening. A total of 417 secondary schoolchildren were screened, of whom 247 (59.2%) were female. The median age was 14years (IQR: 13-15). Clinical screening detected 8/417 children, whereas screening echocardiography detected 42/417 suspected cases of RHD. Definitive echocardiography confirmed 9/417 with RHD corresponding to a prevalence of 21.6 per 1000 (95% CI, 6.7-36.5). All but one of the confirmed RHD cases (8/9) were borderline RHD corresponding to a prevalence of 19.2 per 1000 (95% CI, 8.3-37.5) for borderline RHD and 2.4 per 1000 (95% CI, 0.1-13.3) for definite RHD. RHD was more common in boys and cardiac auscultation missed over 50% of the cases. This study showed a high prevalence of RHD among secondary schoolchildren in North-Central Nigeria with a vast predominance of asymptomatic borderline lesions. Larger school-based echocardiography screening using portable or handheld echocardiography aimed at early detection of subclinical RHD should be adopted.

Mechanistic implications of altered protein expression in rheumatic heart disease.

Rheumatic heart disease (RHD) is a major cause of cardiovascular morbidity and mortality in low- and middle-income countries, where living conditions promote spread of group A β-haemolytic streptococcus. Autoimmune reactions due to molecular mimicry of bacterial epitopes by host proteins cause acute rheumatic fever (ARF) and subsequent disease progression to RHD. Despite knowledge of the factors that predispose to ARF and RHD, determinants of the progression to valvular damage and the molecular events involved remain incompletely characterised. This review focuses on altered protein expression in heart valves, myocardial tissue and plasma of patients with RHD and pathogenic consequences on RHD. Proteins mainly involved in structural organization of the valve matrix, blood homeostasis and immune response were altered due to RHD pathogenesis. Study of secreted forms of these proteins may aid the development of non-invasive biomarkers for early diagnosis and monitoring outcomes in RHD. Valve replacement surgery, the single evidence-based strategy to improve outcomes in severe RHD, is costly, largely unavailable in low- and middle-income countries (LMIC) and requires specialised facilities. When diagnosed early, penicillin prophylaxis may be used to delay progression to severe valvular damage. Echocardiography and cardiovascular magnetic resonance and the standard imaging tools recommended to confirm early diagnosis remain largely unavailable and inaccessible in most LMIC and both require expensive equipment and highly skilled persons for manipulation as well as interpretation of results. Changes in protein expression in heart valves and myocardium are associated with progressive valvular deformation in RHD. Understanding these protein changes should shed more light on the mechanisms of pathogenicity, while secreted forms of these proteins may provide leads towards a biomarker for non-invasive early detection of RHD.

Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds.

Rheumatic Heart Disease (RHD) is an autoimmune response to a bacterial attack which deteriorates the normal functioning of the heart valves. The damage on the valves affects the normal blood flow inside the heart chambers which can be recorded and listened to via a stethoscope as a phonocardiogram. However, the manual method of auscultation is difficult, time consuming and subjective. In this study, a convolutional neural network based deep learning algorithm is used to perform an automatic auscultation and it classifies the heart sound as normal and rheumatic. The classification is done on un-segmented data where the extraction of the first, the second and systolic and diastolic heart sounds are not required. The architecture of the CNN network is formed as an array of layers. Convolutional and batch normalization layers followed by a max pooling layer to down sample the feature maps are used. At the end there is a final max pooling layer which pools the input feature map globally over time and at the end a fully connected layer is included. The network has five convolutional layers. This current work illustrates the use of deep convolutional neural network using a Mel Spectro-temporal representation. For this current study, an RHD heart sound data set is recorded from one hundred seventy subjects from whom one hundred twenty four are confirmed RHD patients. The system has an overall accuracy of 96.1% with 94.0% sensitivity and 98.1% and specificity.

Comparison of the newer proposed diagnostic score with the World Heart Federation criteria for echocardiographic detection of rheumatic heart disease.

Echocardiography has been found to be a much better screening tool compared to clinical examination for the detection of rheumatic heart disease (RHD) in asymptomatic school children living in the RHD endemic areas. Recently, World Heart Federation (WHF) published echocardiographic criteria for the diagnosis of RHD. The present study was done to compare the performance of the newer proposed, quantitative diagnostic score against the qualitative WHF criteria in a field survey of asymptomatic school children belonging to the district having high prevalence of RHD. 3000 asymptomatic school children studying in rural and urban schools of Bikaner district were screened both by clinical examination and echocardiography performed in parallel. The WHF criteria and the proposed diagnostic score were applied simultaneously for the diagnosis of RHD. A high prevalence of subclinical RHD was found. There was complete agreement between the two sets of criteria for the diagnosis of RHD. However, there was discrepancy in grading the severity of disease. The diagnostic score proved superior to the WHF criteria in grading the disease severity accurately. Diagnostic score captures the disease spectrum of RHD better than WHF criteria and reduces the subjectivity in the diagnosis of RHD.

P3133Single parasternal long axis echocardiography by briefly trained health workers using handheld devices for detection of rheumatic heart disease: a prospective study of diagnostic accuracy

Abstract Introduction Echocardiographic screening can detect rheumatic heart disease (RHD) in high-risk populations,but is limited by reliance on highly-trained experts and equipment. We sought to determine the diagnostic utility of an ultra-abbreviated single parasternal-long-axis-sweep of the heart (SPLASH) echocardiography protocol to detect RHD, performed by briefly-trained health workers. Methods In Timor-Leste and Northern Australia, individuals aged 5–20y were offered school-based echocardiographic screening. Health workers completed online modules followed by one-week of practical training, logging 50 echocardiograms prior to study. The index test was SPLASH, performed and reported by health workers using handheld GE V-scan devices. The index test was abnormal if any mitral or aortic regurgitation was detected. The reference test was a comprehensive echocardiogram, performed by an echocardiographers or cardiologist on a GE Vivid-Q, reported according to World Heart Federation criteria. The diagnostic accuracy of the index test was determined. Results 2590 subjects underwent index and reference tests. Prevalence of definite RHD was 3.2% (83/2590). Sensitivity and specificity of index test were 0.75 (95% CI 0.64–0.83) and 0.77 (95% CI 0.75–0.78) respectively for detection of any definite RHD, and 0.91 (95% CI 0.74–0.98) and 0.76 (95% CI 0.74–0.78) respectively for detection of moderate or severe RHD. Conclusion Health workers using SPLASH detected the vast majority of moderate and severe RHD cases, but lacked sensitivity for detection of mild RHD. Further analysis is underway to evaluate the learning curve and other performance indicators of health workers performing and interpreting echocardiograms. This will allow refinement of SPLASH protocol and augmentation of health worker training to increase detection rates and accuracy for future population screening initiatives. Acknowledgement/Funding Heart Foundation Australia Vanguard Grant, Menzies HOT-NORTH pilot project grant, Snow Foundation, Rotary, Bawinanga Aboriginal Corporation, Mala'la

Handheld echocardiography for screening and control of rheumatic heart disease study in Gezira state, Sudan: a double approach model.

Rheumatic heart disease (RHD) is prevalent in Sudan's rural areas including Gezira state in central Sudan. We initiated a control program aiming at measurement of the echocardiographic (echo) prevalence of RHD, training of health workers and public awareness. This is a cross-sectional and interventional study conducted in Gezira State, Al Managil Locality from Nov 2016 to February 2018. We used handheld echo (HHE) to detect the prevalence of RHD in school children and those tested positives were referred for standard echo. In addition, training on detection of RHD for health professionals was offered using training modules for physicians and nurses. Evaluation of health facilities was carried out using a questionaire. This was coupled with educational sessions to increase public awareness about RHD using posters and pamphlets. Two thousand and one hundred twenty-nine school children were screened, 36 cases were positive by HHE, out of these 31 underwent standard echo and 5 were confirmed to have RHD, giving an echo prevalence of 2.3/1,000. All cases had mild mitral regurgitation. Knowledge, attitudes and practices of 175 health workers were assessed then a tailored training program was implemented. Practices that are not compatible with Sudan's RHD Guidelines were detected including performing skin testing prior to administration of benzathine penicillin and under-utilization of local anesthetic to decrease the pain when giving the injection. Benzathine penicillin was available in only 32% of health facilities and only 25% of their personnel received training in RHD management. RHD echo prevalence in Gezira is relatively high and the health system needs to be strengthened. A double approach, screen-to-control program that utilizes HHE screening, health workers' training, public awareness and providing medical supplies in primary health care centers is feasible.

Screening University Students for Rheumatic Heart Disease

Introduction: Screening using echocardiography is currently recognized as the state of art tool for early detection of Rheumatic heart disease (RHD). Unfortunately, primary and periodic examination freshman university students, does not include examination of the heart via echocardiography. Aim: To screen freshman medical students for RHD and other risk factors that can aggravate the progression of the heart disease. Methods: Freshman (first year) medical students were exposed to full cardiovascular examination. Four hundred students were targeted only 150 completed the exam. All students underwent a full anthropometric assessment for weight, height and Body mass index and screening for high blood pressure. A full cardiac exam was done for 100 students using a portable echocardiography machine. Results: Overall prevalence of obesity and overweight was 6.7% and 11.3% respectively. Pre-hypertension and hypertension were detected in 10.7% and 8% respectively. By echocardiography exam, mitral valve prolapse was detected in 4 (4%) and RHD in 3 students (3%) which was mostly mitral regurgitation was the main lesion in 100% of the cases. There was a direct positive significant correlation between overweight, obesity and hypertension at P<0.001. Conclusion: Screening for cardiovascular disease for freshman student is important to reveal hidden cardiovascular disease. Echocardiography is helpful tool in early identification of silent disease.

Pattern of Heart Valve Involvement in Rheumatic Heart Disease

Rheumatic heart disease is the most important consequence of acute rheumatic fever. Both are common cardiovascular problems in Nepal. Echocardiographic detection of rheumatic heart disease is important to establish the diagnosis. The involvement of valves and their severity guides the therapeutic options. A total of 133 valvular heart disease cases attended in Dhulikhel Hospital between July 2008 to June 2009 were analyzed. Fifty-one patients, in whom the problems were rheumatic in origin were studied. Among them, 12% (6) had isolated aortic valve involvement, 35%(18) had isolated mitral valve and 53%(27) ahd mixed involvement. Severe mitral stenosis accounts for 24% of all mitral stenosis and severe aortic stenosis is 20% fo all aortic stenosis. The rates for severe mitral regurgitation and severe aortic regurgitaiton are 30% and 28% respectively. Although the study population has a high number of female patients, the differences in the rates of involvement of aortic or mitral valve in both genders are statistically insignificant. The study, although small, confirms that in this population, females are more commonly affected, that the mitral valve is the most commonly damaged valve and that disease affecting multiple valves is marginally more common than isolated valve disease. The detection of valvular involvement at different stages can guide the therapeutic options.

A focussed single-view hand-held echocardiography protocol for the detection of rheumatic heart disease - CORRIGENDUM.

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A focussed single-view hand-held echocardiography protocol for the detection of rheumatic heart disease.

Echocardiographic screening represents an opportunity for reduction in the global burden of rheumatic heart disease. A focussed single-view screening protocol could allow for the rapid training of healthcare providers and screening of patients. The aim of this study was to determine the sensitivity and specificity of a focussed single-view hand-held echocardiographic protocol for the diagnosis of rheumatic heart disease in children. A total of nine readers were divided into three reading groups; each interpreted 200 hand-held echocardiography studies retrospectively as screen-positive, if mitral regurgitation ⩾1.5 cm and/or any aortic insufficiency were observed, or screen-negative from a pooled study library. The performance of experts receiving focussed hand-held protocols, non-experts receiving focussed hand-held protocols, and experts receiving complete hand-held protocols were determined in comparison with consensus interpretations on fully functional echocardiography machines. In all, 587 studies including 76 on definite rheumatic heart disease, 122 on borderline rheumatic heart disease, and 389 on normal cases were available for analysis. The focussed single-view protocol had a sensitivity of 81.1%, specificity of 75.5%, negative predictive value of 88.5%, and a positive predictive value of 63.2%; expert readers had higher specificity (86.1 versus 64.8%, p<0.01) but equal sensitivity. Sensitivity - experts, 96% and non-experts, 95% - and negative predictive value - experts, 99% and non-experts, 98% - were better for definite rheumatic heart disease. False-positive screening studies resulting from erroneous identification of mitral regurgitation and aortic insufficiency colour jets increased with shortened protocols and less experience (p<0.01). Our data support a focussed screening protocol limited to parasternal long-axis images. This holds promise in making echocardiographic screening more practical in regions where rheumatic heart disease remains endemic.