Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis

机器学习驱动的经胸超声心动图 心脏淀粉样变性的分析和筛查 心脏淀粉样变性（CA）是一种严重的，但越来越多的可治疗的心力衰竭的原因。尸检研究表明， 估计CA的患病率约为所有老年人的25%，主动脉瓣狭窄患者的15%至20%。 狭窄尽管CA在一般人群和特定亚群中的患病率不断增加， 作为心力衰竭原因的诊断受到临床上认识不足和随后诊断不足的阻碍， 实践数据表明，从症状出现到诊断的平均时间为2年， 报告说，在确定明确诊断之前，平均看了5名医生。 经胸超声心动图（TTE）测试是最常见的初始评估，因为它的广泛性， 空房的最近对医疗保险人群的使用情况进行的一项审查表明， 每年的医疗费用为12亿美元。TTE提供全面的 关于心脏结构和功能的信息，但解释的复杂性限制了其筛选 在CA患者中的表现和诊断可能具有挑战性。 因此，我们的团队寻求提供基于计算机视觉和机器学习的TTE分析和筛选 解决方案CA。我们在加速开发方面处于独特的地位，有359名患者， 确认CA和4，862个对照。在第一阶段，我们将构建一个基于深度学习神经网络的图像， 处理流水线。它将TTE序列映射到二维空间中，该二维空间允许识别 心脏心跳周期内的四腔舒张期峰值和收缩期峰值图像。这将使我们的 筛选模型，以识别局部心肌壁运动变化和肥大模式， 与具有正常心脏功能的对照相比表征淀粉样变性。操作点定义 我们的筛选导向模型的性能特征（包括灵敏度、特异性和阴性 预测值）将使用平均加权准确度（AWA）方法进行优化，该方法考虑了CA 疾病患病率沿着与期望的假阳性和假阴性折衷。如果成功，我们 设想一个第二阶段的建议，建立和部署一个自动化的TTE分析工具，并评估它在一个多 中心临床研究。这为我们实施计算机辅助TTE筛查的长期目标奠定了基础 以改善心脏淀粉样变性患者的识别和扩展护理。

Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis Cardiac amyloidosis (CA) is a serious but increasingly treatable cause of heart failure. Autopsy studies have estimated the prevalence of CA at approximately 25% of all octogenarians, and 15 to 20% of patients with aortic stenosis. Despite the increasing prevalence of CA within the general population and specific subpopulations, its diagnosis as a cause of heart failure is hampered by under recognition and subsequent underdiagnosis in clinical practice. Data suggest that the average time from onset of symptoms to diagnosis is 2 years and that patients report seeing an average of 5 physicians prior to establishing a definitive diagnosis. Transthoracic echocardiography (TTE) testing is the most common initial evaluation because of its wide availability. A recent utilization review in the Medicare population indicated over 7 Million echocardiographic tests are performed each year accounting for $1.2 Billion in healthcare costs. TTEs provide comprehensive information about cardiac structure and function, yet complexity of interpretation has limited its screening performance in patients with CA, and diagnosis can be challenging. Thus, our group seeks to offer a computer vision and machine learning based TTE analysis and screening solution for CA. We are uniquely positioned for accelerated development with a cohort of 359 patients with confirmed CA and 4,862 controls. In Phase I, we will build a deep learning neural network-based image processing pipeline. It maps the TTE sequence into a 2-dimensional space that allows for the identification of the 4-chamber peak diastolic and peak systolic images within the cardiac heartbeat cycle. This will enable our screening model to recognize regional myocardial wall motion changes and hypertrophic patterns that characterize amyloidosis in comparison to controls with normal cardiac function. The operational point defining the performance characteristics of our screening-oriented model (including sensitivity, specificity, and negative predictive value) will be optimized using an average weighted accuracy (AWA) approach which accounts for CA disease prevalence along with a desired false positive and false negative tradeoff. If we are successful, we envision a Phase II proposal to build and deploy an automated TTE analysis tool, and to evaluate it in a multi- center clinical study. This sets the stage for our long-term goal to implement a computer assisted TTE screening solution to improve identification and by extension care of patients with cardiac amyloidosis.