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)是一种严重但日益可治疗的心力衰竭原因。尸检研究表明 估计CA的患病率在所有八旬老人中约为25%，在主动脉患者中约为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上升，但其 心力衰竭的诊断因临床上认识不足和随后的诊断不足而受到阻碍。 练习一下。数据表明，从出现症状到确诊的平均时间为2年，患者 报告说，在确定最终诊断之前，平均要看5名医生。 经胸超声心动图(TTE)检查是最常见的初步评估，因为它广泛 可用性。最近在医疗保险人群中的一项利用审查表明，超过700万次超声心动图检查 每年进行，占医疗成本的12亿美元。TTES提供全面的 有关心脏结构和功能的信息，但复杂的解释限制了其筛选 CA患者的表现和诊断可能具有挑战性。 因此，我们的小组寻求提供基于计算机视觉和机器学习的TTE分析和筛选 CA解决方案。我们拥有359名患者，处于加速发展的独特地位 确认为CA和4,862名对照。在第一阶段，我们将建立一个基于深度学习神经网络的图像 正在处理管道。它将TTE序列映射到2维空间，从而允许识别 心跳周期内4腔峰值舒张期和峰值收缩期图。这将使我们的 识别局部心肌壁运动变化和肥厚模式的筛查模型 将淀粉样变性与心功能正常的对照组进行比较。运行点定义 我们的筛查导向模型的性能特征(包括敏感性、特异性和阴性 预测值)将使用考虑CA的平均加权精度(AWA)方法进行优化 疾病流行以及期望的假阳性和假阴性之间的权衡。如果我们成功了，我们 设想第二阶段计划，以构建和部署自动化TTE分析工具，并在多个 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 解决方案，以改善对心脏淀粉样变性患者的识别和推广护理。

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.