Radiomics approach to engineering an artificial intelligence based echocardiography platform to predict cardiovascular surgery and heart failure outcomes.

放射组学方法设计基于人工智能的超声心动图平台来预测心血管手术和心力衰竭的结果。

• 批准号: 10367037
• 负责人: William Hiesinger
• 金额: $ 58.8万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367037
• 关键词: Accounting; Acute; Address; Affect; Apical; Artificial Intelligence; Artificial Intelligence platform; Benchmarking; Cardiac; Cardiovascular Diseases; Cardiovascular Surgical Procedures; Clinical; Cloud Computing; Color; Complex; Computer Systems; Data; Data Set; Decentralization; Decision Support Systems; Development; Devices; Diagnosis; Disease; Echocardiography; Effectiveness; Engineering; Event; Expert Systems; Failure; Framingham Heart Study; Future; Generations; Goals; Hand; Heart failure; Histopathology; Hospitals; Human; Individual; Informatics; International; Manuals; Measurement; Medical; Medical Imaging; Methods; Modernization; Motion; Myocardial; Nature; Operative Surgical Procedures; Outcome; Patients; Pattern; Performance; Persons; Pharmacotherapy; Postoperative Complications; Quality Control; Research; Research Personnel; Risk; Scanning; Screening procedure; Secure; Severities; Slide; Source; Supervision; Survival Analysis; System; Techniques; Thoracic Radiography; Time; Training; Transplantation; United States; Ventricular; Visual; Work; base; cardiogenesis; clinical diagnosis; clinical practice; cohort; community setting; computerized tools; cost effective; density; design; disease diagnosis; early screening; heart function; high dimensionality; high risk; imaging modality; implantable device; individual patient; left ventricular assist device; machine learning framework; mortality; multimodality; novel; outcome prediction; point of care; portability; pulmonary arterial hypertension; radiomics; right ventricular failure; screening; survival outcome; survival prediction; tool

SUMMARY In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a wide variety of medical imaging tasks, in applications ranging from disease diagnosis using Chest X-Rays to survival analyses using histopathology slides. All current automated echocardiography systems – much like human echocardiography reads – are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculated. Despite the staggering increase in usable data, the vast majority of information contained in time-resolved echocardiography videos remain woefully underutilized. As opposed to treating echocardiography studies as videos intended solely for visual interpretation, the ‘radiomics’ approach treats medical images as high-dimensional datasets to be mined with advanced computational tools. The overall goals of this project are to further develop and validate our novel, generalizable, multi-modal artificial intelligence (AI) platform for analyzing time resolved echocardiography studies, to address this underutilization. The impact of such an ECHO AI system is immediately perceptible in the field of heart failure. An estimated 6.5 million people suffer from heart failure in the United States. Across the spectrum of severity in this disease, echocardiography remains the cornerstone of screening and clinical diagnosis, a guide for medical management and pharmacotherapy, and an essential tool for planning acute lifesaving surgical interventions. We propose to build on our preliminary research and ready access to high quality paired echocardiographic and clinical datasets to achieve the following goals: 1) Develop a surgical decision support system for end-stage heart failure patients considered for left ventricular assist device (LVAD) implant. 2) Expand and generalize our ECHO AI tools to enable downstream prediction of long-term survival and development of heart failure, in both asymptomatic individuals and patients with pulmonary arterial hypertension 3) Cloud and hardware integration of our ECHO AI platform. The end result of our research will be a powerful ECHO AI tool with that is translatable, and integrated into clinical practice.

摘要 近年来，人工智能使自动化系统达到或超过了 在从疾病诊断到应用的各种医学成像任务中的临床专家 使用胸部X光进行组织病理学切片的生存分析。所有目前的自动超声心动图 系统--很像人类的超声心动图读数--本质上是简化论的；是一个复杂的序列 心脏收缩的模式被简化为一个或多个腔室的轮廓，从这些腔室中 然后计算心脏功能的指标。尽管可用数据惊人地增长，但绝大多数 遗憾的是，时间分辨超声心动图视频中包含的信息仍然没有得到充分利用。而不是 将超声心动图研究视为仅用于视觉解释的视频，即“放射组学”方法 将医学图像视为要用高级计算工具挖掘的高维数据集。整体而言 这个项目的目标是进一步开发和验证我们新颖的、可推广的、多模式的人工智能 (AI)用于分析时间分辨超声心动图研究的平台，以解决这种未得到充分利用的问题。 这样的ECHO AI系统在心力衰竭领域的影响立竿见影。估计为6.5 在美国，有数百万人患有心力衰竭。在这种疾病的严重程度上， 超声心动图仍然是筛查和临床诊断的基石，是医疗管理的指南 和药物治疗，以及计划紧急救生手术干预的必要工具。我们建议 以我们的初步研究为基础，随时获取高质量的配对超声心动图和临床数据集 实现以下目标：1)为终末期心力衰竭患者开发手术决策支持系统 考虑植入左心室辅助装置(LVAD)。2)扩展和推广我们的Echo AI工具，以 使下游能够预测长期存活和心力衰竭的发展，在这两种情况下都没有症状 患有肺动脉高压的个人和患者3)ECHO AI的云和硬件集成 站台。我们研究的最终结果将是一个强大的Echo AI工具，它是可翻译的，并且是集成的 进入临床实践。