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

用于自动识别和跟踪罕见心肌疾病的机器学习

• 批准号: 9739345
• 负责人: Rahul Chandrakant Deo
• 金额: $ 68.72万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-05 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9739345
• 关键词: Address; Affect; Algorithm Design; Amyloid; Amyloidosis; Arrhythmia; Artificial Intelligence; Cardiac; Cardiomyopathies; Cardiovascular system; Cessation of life; Clinical; Clinical Trials; Clinics and Hospitals; Cohort Studies; Communities; Computer Vision Systems; Coronary heart disease; DNA Sequence Alteration; Data; Deposition; Detection; Development; Diagnosis; Disease; Disease Progression; Early Diagnosis; Echocardiography; Face; General Population; Goals; Heart Diseases; Heart failure; Hospitalization; Human; Hypertension; Hypertrophic Cardiomyopathy; Image; Image Analysis; Individual; Information Retrieval; Inherited; Left; Left Ventricular Hypertrophy; Machine Learning; Manuals; Measurement; Measures; Methods; Molecular; Monitor; Morphology; Myocardium; Outcome; Output; Patients; Phenotype; Play; Process; Proteins; Reader; Reading; Registries; Research; Research Personnel; Role; Safety; Standardization; Structure; Sudden Death; Symptoms; Testing; Thick; Time; Two-Dimensional Echocardiography; United States; Validation; Ventricular; adverse outcome; automated image analysis; base; career; clinical care; clinical imaging; cohort; comorbidity; cost; digital; disease diagnosis; epidemiology study; heart imaging; hypertensive heart disease; image processing; innovation; intelligent algorithm; interest; multidisciplinary; novel therapeutics; particle; patient registry; repository; response; statistical learning; tool

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.

项目概要 尽管心脏淀粉样变性和肥厚性心肌病 (HCM) 是导致心脏病的相对罕见的原因 失败（HF），由于以下几个共同原因，它们的检测和治疗特别具有挑战性：（1）常规 临床影像（即超声心动图 [echo]），它们可能很难与表面相似的、 导致左心室 (LV) 肥大的更常见的心脏病； (2) 诊断结果为 经常被漏诊，因此患者可能会在病程晚期、治疗困难时就诊； (3) 尚未确定能够可靠反映疾病进展的客观、非侵入性指标；和（4） 已知患有这些疾病的患者数量较少可能会使流行病学研究和临床试验变得困难 来组织和进行。对于心脏淀粉样变性和 HCM，回波在诊断中都起着至关重要的作用 鉴于其普遍的临床可用性、安全性和低成本，纵向监测。更广泛地说，回声 主导了当前常规心脏成像的格局，在心脏成像中执行了数千万个回波 美国每年。然而，上述临床挑战突出了一些缺点 echo：它的能力有限（1）早期诊断疾病； (2) 区别对待 形态相似的疾病； (3) 量化疾病进展。该提案旨在解决 当前回波读取工作流程的缺陷，该工作流程是主观的并且仅捕获了一小部分 每项研究中可用的数据。该应用程序的总体目标是利用机器学习的进步 开发和验证全自动回波图像分析方法来诊断和跟踪罕见疾病 心肌病，重点关注心脏淀粉样变性和 HCM。我们的建议以假设为中心 高度可扩展的计算机视觉方法可以应用于回声研究以克服局限性 标准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 用于回声量化和疾病识别，以检测和区分引起的心脏病 增加 LV 壁厚； (2) 表征疾病进展的可量化回波测量 心脏淀粉样变性和 HCM 并将其与临床结果相关联。我们的多学科团队 由心肌病、超声心动图、计算机视觉和机器学习领域的专家组成，将 分析来自 2 个大型患者登记处的回声和患者数据：多中心淀粉样蛋白表型研究 (MAPS) 和肉瘤人类心肌病登记处 (SHARe) HCM 网络，并使用 近 400,000 个回声的存储库。我们目标的成功实现将带来一个创新工具 心肌疾病的早期诊断和疾病进展的跟踪。重要的是，我们的项目将设置 通过自动化对罕见心肌疾病进行更大规模的流行病学研究的阶段 识别这些患者，从而开发以前无法实现的基础广泛的队列 对于这些条件。