Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol. Issue 8 (5th August 2021)

• Record Type: Journal Article
• Authors: Ali, Fatima; Hasan, Babar; Ahmad, Huzaifa; Hoodbhoy, Zahra; Bhuriwala, Zainab; Hanif, Muhammad; Ansari, Shahab U; Chowdhury, Devyani
• Is Part Of: BMJ open. Volume 11:Issue 8(2021)
• Publication Date: 2021-08-05
• Subjects: health informatics -- echocardiography -- paediatric cardiology -- valvular heart disease; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-044070
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: Rheumatic 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 (> 1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories—patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing. 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.