An automated system to interpret echocardiography to predict adverse outcomes in patients with right ventricular dysfunction in daily hospital practice

项目摘要 右心室（RV）功能障碍是一种常见而复杂的小儿心脏病。是 也是先天性心脏病患者发病率和死亡率的常见因素 （CHD）。由于RV的复杂几何形状及其对变化的相对适应性， 生理条件下，RV功能障碍了解甚少，难以表征 准确性和准确性，因此诊断往往被延误。最常用的诊断工具是 超声心动图然而，人工检查超声心动图是耗时的。 此外，可能存在未被发现的与RV相关的超声心动图模式， 功能障碍在成人研究中，机器学习模型（MLM）已经成功地 通过超声心动图评估RV功能。我们假设，应用新的 将MLM应用于儿科超声心动图将使我们能够提高 评估，以及确定RV功能障碍的新标志物。我们建议发展一个 自动化工具，用于生成RV健康评分，以识别RV功能障碍并预测 不良结局（包括心力衰竭、心脏和/或肺）的发生和时间 移植和死亡。自动化工具将构成预警系统模块， 这将被部署到一个基于大数据的风险预测平台上， 业务这项研究有三个具体目标。首先，我们将提取超声心动图， 斯坦福大学儿童医院的结构化电子病历。个儿童队列 将构建正常或异常RV。第二，传销将发展和验证 1）预测RV功能障碍的存在和不良结局的概率，以及2） 预测不良结局的进展率。一个基于深度学习的工作流程将是 建立了儿科超声心动图和临床数据的输入，并生成预测。 第三，我们将把目标2中开发的模型集成到HBI Spotlight解决方案中。的 Spotlight解决方案包括一个具有高容量数据的医疗保健监控平台 基础设施和风险引擎，为参与Healthix的医疗机构提供人工智能解决方案， 美国最大的公共卫生信息交换网络。这将为我们的 用于在其他队列中进行进一步临床验证的算法。

Project Summary Right ventricle (RV) dysfunction is a common and complex form of pediatric heart disease. It is also a common contributor to morbidity and mortality for patients with congenital heart diseases (CHD). Due to the complex geometry of the RV and its relative adaptability to changing physiologic conditions, RV dysfunction is poorly understood and difficult to characterize precisely and accurately, thus diagnosis is often delayed. The most common diagnosis tool is echocardiograms. Manual review of echocardiograms is time consuming, however. Furthermore, there might be uncovered echocardiogram patterns associated with RV dysfunctions. In adult studies, machine learning models (MLM) have been successfully implemented to assess RV functions by echocardiograms. We hypothesize that applying novel MLM to pediatric echocardiograms will allow us to improve the accuracy and reliability of assessment, as well as identify novel markers of RV dysfunction. We propose to develop an automated tool to generate a RV health score to identify RV dysfunction and predict the development and time of adverse outcomes including heart failure, heart and/or lung transplantation, and death. The automated tool will constitute an early warning system module, which will be deployed onto a big-data-based risk prediction platform developed by our small business. The study has three specific aims. First, we will extract echocardiograms and structured electronic medical records from the Stanford Children’s Hospital. Cohorts of children with normal or abnormal RV will be constructed. Second, MLM will be developed and validated to 1) predict the presence of RV dysfunction and the probability of adverse outcomes, and 2) predict the rate of progression to adverse outcomes. A deep learning-based workflow will be established to take input of pediatric echocardiogram and clinical data and generate predictions. Third, we will integrate the models developed in Aim #2 into the HBI Spotlight Solutions. The Spotlight Solutions include a healthcare surveillance platform with high-capacity data infrastructure and risk engines to offer AI solutions to care facilities participating the Healthix, the largest public health information exchange network in the US. This will prepare our algorithms for further clinical validation in other cohorts.