Pediatrics and Child Health, Aga Khan University Hospital, Karachi, Pakistan.
Medicine, MedStar Georgetown University Hospital, Washington, District of Columbia, USA.
BMJ Open. 2021 Aug 5;11(8):e044070. doi: 10.1136/bmjopen-2020-044070.
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.
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 approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators.
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.
风湿性心脏病（RHD）在全球范围内造成了大量的发病率和死亡率。为了降低 RHD 的负担，及时开始二级预防非常重要。本研究的目的是确定亚临床 RHD 的频率，并使用数字听诊器（DAS）的波形数据训练深度学习（DL）算法来预测亚临床 RHD。
我们计划在卡拉奇（巴基斯坦）的一组服务贫困人群的学校中招募 1700 名儿童，为期 12 个月。所有年龄在 5-15 岁之间、无潜在先天性心脏病的同意参加的学生都有资格参加研究。我们将收集社会人口统计学、人体测量学数据、风湿热症状或诊断史、心音图（PCG）和从 DAS 获得的心电图（ECG）数据的信息。将对每位研究参与者进行手持式超声心动图检查，以评估是否存在二尖瓣反流（MR）射流（1.5cm）或任何视图中是否存在主动脉瓣反流（AR）。如果存在这些发现中的任何一种，将使用世界心脏联合会（WHF）的标准超声心动图进行确认性检查，以确认亚临床 RHD 的诊断。将使用数字听诊器的听诊数据来训练深度神经网络，以自动识别患有亚临床 RHD 的患者。所提出的神经网络将使用参与者标准超声心动图的标签以监督方式进行训练。训练完成后，神经网络将能够自动将 DAS 数据分类为以下三个主要类别之一-明确的 RHD 患者、边界 RHD 患者和正常患者。将通过标准统计方法对假设检验的结果进行确认。
该研究已获得巴基斯坦 Aga Khan 大学的伦理批准。研究结果将通过科学出版物和合作者进行传播。
本研究侧重于确定巴基斯坦卡拉奇在校儿童中亚临床 RHD 的频率，并使用数字听诊器开发一种 DL 算法来筛查这种疾病。
