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.

项目摘要 尽管心脏淀粉样变性和肥厚性心肌病（HCM）是心脏相对罕见的原因 失败（HF），出于几个共同的原因，他们特别具有挑战性地检测和治疗：（1） 临床成像（即超声心动图[echo]），它们很难与表面上的相似， 引起左心室（LV）肥大的心脏病的更常见形式； （2）诊断是 经常错过，因此在治疗困难的​​时候可能会在疾病的后期出现。 （3）尚未确定可靠地反映疾病进展的客观无创指标； （4） 这些疾病的少数已知患者可能会使流行病学研究和临床试验变得困难 组织和进行。对于心脏淀粉样变性和HCM，Echo在两个诊断中都起着至关重要的作用 鉴于其无处不在的临床可用性，安全性和低成本，纵向监测。更广泛地回声 主导了常规心脏成像的当前景观，在 每年美国。但是，上面描述的临床挑战突出了几个缺点 ECHO：它的能力有限（1）在早期诊断疾病的能力； （2）区分 形态上相似的疾病； （3）量化疾病进展。该建议旨在解决 当前回声阅读工作流程中的缺陷，这是主观的，仅捕获一小部分 每个研究中可用的数据。该应用程序的总体目的是利用机器学习的进步 开发和验证全自动回声图像分析方法诊断和跟踪罕见 心肌病，专注于心脏淀粉样变性和HCM。我们的建议集中于假设 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 标准的临床回声阅读工作流程。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鉴定，以检测和区分引起的心脏病 LV壁厚增加； （2）表征可量化的疾病进展的量化措施 心脏淀粉样变性和HCM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的专家组成，将 分析来自2个大型患者注册表的回声和患者数据：多中心淀粉样蛋白表型研究 （地图）和肌膜人类心肌病注册中心（共享）HCM网络，并使用 近400,000个Echos的存储库。我们的目标的成功完成将为您提供创新的工具 心肌疾病的早期诊断和疾病进展的追踪。重要的是，我们的项目将设定 通过自动化的罕见心肌疾病进行更大的流行病学研究的阶段 识别这些患者，从而发展以前无法实现的基于广泛的队列 对于这些条件。