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Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

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The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
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Rheumatic Heart Disease I: Introduction01:23

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Rheumatic heart disease or RHD is a chronic condition that results from rheumatic fever, causing permanent damage to the heart valves.Etiology and Risk FactorsIt primarily arises from rheumatic fever, an inflammatory disease that can develop after untreated or inadequately treated group A streptococcal (GAS) pharyngitis. Streptococcus spreads through direct contact with oral or respiratory secretions. While the bacteria are the causative agents, factors like malnutrition, overcrowding, poor...
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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
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Rheumatic heart disease (RHD) management can be divided into two main strategies: prevention and long-term management.Primary PreventionPrimary prevention focuses on timely diagnosis and management of group A streptococcal pharyngitis to prevent acute rheumatic fever. The most widely used antibiotic for treating this condition is intramuscular benzathine penicillin G.Acute Rheumatic Fever TreatmentThe primary treatment goal for a patient diagnosed with acute rheumatic fever is to suppress the...
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AssessmentA comprehensive assessment is essential in managing a patient with rheumatic heart disease (RHD). Begin with obtaining a detailed medical history, including recent streptococcal infections, a history of rheumatic fever, or previously diagnosed rheumatic heart disease. Assess the patient for symptoms such as fever, chest pain, widespread joint pain (arthralgia), tachycardia, pericardial friction rub, muffled heart sounds, heart murmurs, peripheral edema, subcutaneous nodules, and...
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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart

Melkamu Hunegnaw Asmare, Frehiwot Woldehanna, Luc Janssens

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    This study introduces a deep learning algorithm for automatic Rheumatic Heart Disease (RHD) detection from heart sounds. The convolutional neural network achieves high accuracy in classifying normal and RHD cases, improving diagnostic efficiency.

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    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Medical Diagnostics

    Background:

    • Rheumatic Heart Disease (RHD) damages heart valves, affecting blood flow, detectable via phonocardiograms.
    • Manual auscultation for RHD diagnosis is subjective, time-consuming, and challenging.
    • Automated analysis of heart sounds offers a potential solution for objective RHD detection.

    Purpose of the Study:

    • To develop and evaluate a deep learning algorithm for automatic classification of heart sounds as normal or indicative of RHD.
    • To assess the performance of a convolutional neural network (CNN) on un-segmented heart sound data.

    Main Methods:

    • A deep convolutional neural network (CNN) architecture was designed with convolutional, batch normalization, and max pooling layers.
    • The CNN utilized Mel Spectro-temporal representation for analyzing heart sound data.
    • The model was trained and validated on a dataset of 170 subjects, including 124 confirmed RHD patients.

    Main Results:

    • The CNN model achieved an overall accuracy of 96.1% in classifying heart sounds.
    • The system demonstrated high diagnostic performance with 94.0% sensitivity and 98.1% specificity.
    • The deep learning approach successfully classified RHD without requiring segmentation of heart sounds.

    Conclusions:

    • Deep convolutional neural networks are effective for automated RHD detection from heart sounds.
    • The proposed method offers a promising, objective, and efficient tool for RHD screening and diagnosis.
    • This AI-driven approach can potentially overcome limitations of traditional manual auscultation.