Machine learning for the automated identification and tracking of rare myocardial diseases

项目总结 虽然心脏淀粉样变性和肥厚性心肌病(HCM)是相对罕见的心脏病因 由于以下几个共同原因，发现和治疗心力衰竭(HF)特别困难：(1)常规 临床成像(即超声心动图[回声])，它们可能很难与表面相似的， 引起左心室肥厚的更常见形式的心脏病；(2)诊断如下 常常被遗漏，因此患者可能在治疗困难的时候出现在病程较晚的时候； (3)尚未确定可靠地反映疾病进展的客观、非侵入性指标；及(4) 少数已知患有这些疾病的患者可能会使流行病学研究和临床试验变得困难。 组织和指挥。对于心脏淀粉样变性和肥厚性心肌病，超声在这两种诊断中都起着关键作用。 以及纵向监测，因为它的临床可用性、安全性和低成本无处不在。更广泛地说，ECHO 主导着当前常规心脏成像的格局，在 美国每年都有。然而，上述临床挑战突出了以下几个缺点 ECHO：它在以下方面能力有限：(1)在疾病的早期阶段诊断；(2)区分 形态相似的疾病；以及(3)量化疾病进展。这项提案旨在解决 当前的回声阅读工作流程中的不足之处，这是主观的，只捕获了 每项研究中可获得的数据。这个应用程序的总体目标是利用机器学习中的先进技术 开发和验证全自动回波图像分析方法，以诊断和跟踪罕见病例 心肌病，重点是心脏淀粉样变性和肥厚性心肌病。我们的建议是围绕这一假设提出的 高度可扩展的计算机视觉方法可以应用于ECHO研究以克服局限性 标准的临床回声读数工作流程。因此，我们的目标是：(1)应用自动化方法 用于回声定量和疾病识别，以检测和鉴别引起 增加的左室壁厚度；和(2)表征疾病进展的可量化的回声测量 心脏淀粉样变性和肥厚型心肌梗死，并将其与临床结果相关联。我们的多学科团队，这是 由心肌病、超声心动图、计算机视觉和机器学习方面的专家组成的威尔 分析来自两个大型患者登记的回声和患者数据：多中心淀粉样表型研究 (MAP)和肌肉人类心肌病登记(SHARE)HCM网络，使用 拥有近400,000个回声的存储库。成功完成我们的目标将产生一种创新的工具 心肌疾病的早期诊断和疾病进展的跟踪。重要的是，我们的项目将 通过自动化对罕见心肌疾病进行更大规模流行病学研究的阶段 确认这些患者，从而形成以前无法实现的广泛队列 在这些情况下。

PROJECT SUMMARY Although cardiac amyloidosis and hypertrophic cardiomyopathy (HCM) are relatively rare causes of heart failure (HF), they are particularly challenging to detect and treat for several shared reasons: (1) on routine clinical imaging (i.e., echocardiography [echo]), they can be difficult to distinguish from superficially similar, more common forms of cardiac disease that cause left ventricular (LV) hypertrophy; (2) the diagnoses are often missed and thus patients can present late in the course of disease at a time when treatment is difficult; (3) objective, noninvasive metrics that reliably reflect disease progression have not been identified; and (4) the small number of known patients with these diseases can make epidemiology studies and clinical trials difficult to organize and conduct. For both cardiac amyloidosis and HCM, echo plays a critical role in both diagnosis and longitudinal monitoring given its ubiquitous clinical availability, safety, and low cost. More broadly, echo dominates the current landscape of routine cardiac imaging, with tens of millions of echos performed in the United States each year. However, the clinical challenges described above highlight several shortcomings of echo: it is limited in its ability to (1) diagnose disease at its early stages; (2) discriminate between morphologically similar diseases; and (3) quantify disease progression. This proposal seeks to address deficiencies in the current echo reading workflow, which is subjective and captures only a small fraction of the data available in each study. The overall objective of this application is to use advances in machine learning to develop and validate fully-automated echo image analytic approaches to diagnose and track rare cardiomyopathies, focusing on cardiac amyloidosis and HCM. Our proposal is centered on the hypothesis that highly scalable computer vision methods can be applied to echo studies to overcome limitations of the standard clinical echo reading workflow. Accordingly our aims are: (1) Apply an automated method for echo quantification and disease identification to detect and differentiate cardiac diseases that cause increased LV wall thickness; and (2) Characterize quantifiable echo measures of disease progression in cardiac amyloidosis and HCM and associate these with clinical outcomes. Our multidisciplinary team, which is composed of experts in cardiomyopathies, echocardiography, computer vision, and machine learning, will analyze echos and patient data from 2 large patient registries: the Multicenter Amyloid Phenotyping Study (MAPS) and the Sarcomeric Human Cardiomyopathy Registry (SHaRe) HCM Network, with validation using a repository of nearly 400,000 echos. The successful completion of our aims will result in an innovative tool for early diagnosis of myocardial diseases and tracking of disease progression. Importantly, our project will set the stage for conducting larger epidemiology studies of rare myocardial diseases by automating the identification of these patients, and thereby developing previously unattainable broad-based cohorts for these conditions.