ArticleBMJ open2021
Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- The global roadmap for cardiovascular imaging: bridging the diagnostic divide.European heart journal. Imaging methods and practice · 2026Review
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- Echocardiographic Screening of Rheumatic Heart Disease: Current Concepts and Challenges.Turkish archives of pediatrics · 2024Article
- Detection and management of latent rheumatic heart disease: a narrative review.Annals of medicine and surgery (2012) · 2023Review
- A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
- Real-world evaluation of the Stemoscope electronic tele-auscultation system.Biomedical engineering online · 2022Article
- Recent Advances in the Rheumatic Fever and Rheumatic Heart Disease Continuum.Pathogens (Basel, Switzerland) · 2022Review
- Machine learning methods for predicting major types of rheumatic heart diseases in children of Southern Punjab, Pakistan.Frontiers in cardiovascular medicine · 2022Article
- Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionRheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD. METHODS AND ANALYSIS: We aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5-15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet ( ETHICS AND DISSEMINATION: Ethics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators. ARTICLE FOCUS: This study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope.
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