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

基本信息

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
项目类别：
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

项目摘要

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）是一种严重但越来越可治疗的心力衰竭原因。尸检研究有据估计，所有八旬老人中 CA 的患病率约为 25%，主动脉粥样硬化患者中 CA 的患病率约为 15% 至 20% 狭窄。尽管 CA 在一般人群和特定亚人群中的患病率不断增加，但其临床上认识不足和随后的诊断不足阻碍了心力衰竭病因的诊断实践。数据表明，从出现症状到诊断的平均时间为 2 年，并且患者据报告，在做出明确诊断之前，平均看了 5 名医生。经胸超声心动图 (TTE) 测试是最常见的初步评估，因为其广泛可用性。最近对医疗保险人群的使用情况审查表明超过 700 万次超声心动图检查每年执行的医疗费用为 12 亿美元。 TTE 提供全面的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 患者的表现和诊断可能具有挑战性。因此，我们团队致力于提供基于计算机视觉和机器学习的 TTE 分析和筛选 CA 的解决方案。我们拥有独特的优势，可以加速开发，拥有 359 名患有此病的患者确认了 CA 和 4,862 个对照。在第一阶段，我们将构建基于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，以便识别心脏心跳周期内的 4 腔峰值舒张期和峰值收缩期图像。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与具有正常心功能的对照相比，表征淀粉样变性。操作点定义我们以筛选为导向的模型的性能特征（包括敏感性、特异性和阴性预测值）将使用平均加权精度（AWA）方法进行优化，该方法考虑了CA 疾病患病率以及所需的假阳性和假阴性权衡。如果我们成功了，我们设想第二阶段提案，以构建和部署自动化 TTE 分析工具，并在多方面对其进行评估中心临床研究。这为我们实施计算机辅助 TTE 筛查的长期目标奠定了基础改善心脏淀粉样变性患者的识别和扩展护理的解决方案。