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分析和心脏淀粉样变性筛查

• 批准号: 10081836
• 负责人: Ricardo Henao Giraldo
• 金额: $ 24.94万
• 依托单位: VIGILANT MEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-07 至 2022-08-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10081836
• 关键词: Accounting; Adult; Age-Years; Amyloid; Amyloidosis; Aortic Valve Stenosis; Area; Autopsy; Awareness; Biopsy; Cardiac; Cardiovascular system; Characteristics; Classification; Clinical; Collection; Computer Assisted; Computer Vision Systems; Computers; Data; Data Set; Databases; Deposition; Development; Diagnosis; Disease; Disease Progression; Echocardiography; Evaluation; Future; General Population; Goals; Health Care Costs; Heart failure; Image; Institutional Review Boards; Laboratories; Left Ventricular Hypertrophy; Left ventricular structure; Machine Learning; Maps; Medicare; Modeling; Motion; Multi-site clinical study; Multicenter Studies; Myocardial; Octogenarian; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pattern Recognition; Performance; Phase; Physicians; Population; Positioning Attribute; Predictive Value; Prevalence; Process; Reporting; Research; Review Literature; Screening procedure; Sensitivity and Specificity; Structure; Surveys; Symptoms; Target Populations; Technology; Testing; Time; Tissues; Training; Ultrasonography; Utilization Review; Validation; Ventricular; Work; autoencoder; base; case control; classification algorithm; clinical application; clinical practice; cohort; convolutional neural network; deep learning; deep neural network; genetic analysis; heart function; image processing; imaging genetics; improved; neural network; neural network classifier; novel; point of care; screening; tool; two-dimensional

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.

机器学习驱动的经脑超声心动图 心脏淀粉样变性的分析和筛查 心脏淀粉样变性（CA）是心力衰竭的严重但越来越可治疗的原因。尸检研究 估计CA的患病率约为所有八十年代人的25％，主动脉患者的患病率为15％至20％ 狭窄。尽管CA在一般人群和特定亚群中的患病率越来越高，但 诊断是造成心力衰竭的原因，受到识别的识别和随后的诊断症的阻碍 实践。数据表明，从症状发作到诊断的平均时间为2年，患者 报告在确定确定的诊断之前，平均要看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 可用性。医疗保险人群最近的一项利用审查表明超过700万个超声心动图测试 每年进行12亿美元的医疗保健费用。 TTE提供了全面 有关心脏结构和功能的信息，但解释的复杂性限制了其筛查 CA患者和诊断患者的表现可能具有挑战性。 因此，我们的小组试图提供基于计算机的视觉和机器学习的TTE分析和筛选 CA的解决方案。我们在加速发展方面拥有独特的位置，有359例患者 确认的CA和4,862个对照。在第一阶段，我们将建立一个深度学习神经网络的图像 处理管道。它将TTE序列映射到二维空间，以识别 心脏心跳周期内的4腔峰舒张期和峰值收缩图像。这将使我们 筛选模型以识别区域心肌壁运动变化和肥厚的模式 与患有正常心脏功能的对照相比，淀粉样变性的表征。定义的操作点 我们面向筛选模型的性能特征（包括灵敏度，特异性和负面 预测价值）将使用平均加权精度（AWA）方法进行优化，该方法解释了CA 疾病患病率以及所需的假阳性和假阴性权衡。如果我们成功，我们 设想II期提案，以构建和部署自动化TTE分析工具，并在多种多样中对其进行评估 中心临床研究。这为我们实施计算机辅助TTE筛选的长期目标奠定了基础 改善心脏淀粉样变性患者的鉴定和扩展护理解决方案。