Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial Intelligence

人工智能心血管多模态成像的患者特异性结果预测

• 批准号: 10353281
• 负责人: Piotr J Slomka
• 金额: $ 102.7万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2029-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10353281
• 关键词: Anatomy; Arteries; Artificial Intelligence; Atherosclerosis; Automation; Blood flow; Calcium; Cardiometabolic Disease; Cardiovascular system; Cause of Death; Chronic Kidney Failure; Clinical; Clinical Data; Coronary Arteriosclerosis; Coronary heart disease; Data; Deposition; Detection; Diabetes Mellitus; Disease; Functional disorder; Heart; High Prevalence; Image; Image Analysis; Inflammation; Measures; Methods; Modality; Molecular; Multimodal Imaging; Myocardial; Myocardial Infarction; Myocardial Ischemia; Patients; Physicians; Population; Positron-Emission Tomography; Psyche structure; Public Health; Quality Control; Recommendation; Registries; Relative Risks; Research; Research Personnel; Risk; Risk Estimate; Risk Factors; Techniques; Vision; Visual; Work; X-Ray Computed Tomography; adverse event risk; aggressive therapy; automated analysis; base; coronary artery calcium; disorder risk; experience; high risk; high risk population; imaging modality; improved; non-invasive imaging; novel; obese patients; outcome prediction; programs; support tools; tool

PROJECT SUMMARY Coronary artery disease (CAD) remains a major public health concern with a high prevalence in the US population. Functional, molecular, and structural imaging offer a unique opportunity to understand the pathophysiology of CAD, especially in high-risk groups such as patients with obesity, diabetes, and chronic kidney disease (cardiometabolic disease). CAD evaluation by imaging is based on modalities that assess (1) myocardial ischemia and myocardial blood flow (2) anatomic burden of atherosclerosis, and (3) disease activity using novel techniques. However, physicians are not yet able to use these data optimally to identify patients at highest risk of adverse events—due to technical complexity of advanced multivariable data, and lack of automation and integrative tools. While positron emission tomography (PET) can measure myocardial blood flow, and depict high-risk plaque in the arteries and CT can reliably detect coronary artery calcium —an unequivocal marker for atherosclerotic disease– physicians are not able to combine these data effectively to identify patients at highest risk of adverse events, due to complexity and lack of automation. Critically, there is an unmet need for efficient integration of diverse imaging and clinical data by a robust, automated clinical tool after non-invasive imaging. Highly efficient artificial intelligence (AI) methods are revolutionizing image analysis and could improve CAD detection and management. The overall vision for the research program is to further the clinical utility of PET/CT in detecting high-risk CAD and guiding subsequent management by automation and integrating all image and clinical data with state-of-the-art AI. We will establish a large multicenter PET and CT imaging registry and with image-based AI, automate analysis and quality control for robust analysis even at less experienced centers, and develop decision support tools utilizing collectively all available PET/CT images and clinical information (beyond what is possible by subjective visual analysis and mental integration). We will develop direct interpretation of images by AI, and patient-specific explanation of the AI findings to the physician. Precise quantitative results will be presented to clinicians (and patients) in easy to understand terms (e.g., % risk per year or as the relative risk of one therapy compared to the alternative) for a specific patient. This work will allow accurate identification of patients with high-risk disease who can benefit treatment from advanced therapies and enable precise patient-specific risk estimates and treatment recommendations in challenging clinical scenarios—in CAD with cardiometabolic disease and advanced high- risk disease.

项目概要 冠状动脉疾病（CAD）仍然是一个主要的公共卫生问题，在美国患病率很高 人口。功能、分子和结构成像提供了一个独特的机会来了解 CAD 的病理生理学，尤其是肥胖、糖尿病和慢性病患者等高危人群 肾脏疾病（心脏代谢疾病）。通过成像进行 CAD 评估基于评估 (1) 的模式 心肌缺血和心肌血流量 (2) 动脉粥样硬化的解剖负担，以及 (3) 疾病活动度 使用新颖的技术。 然而，医生尚无法最佳地使用这些数据来识别不良风险最高的患者。 事件——由于先进多变量数据的技术复杂性以及缺乏自动化和集成工具。 虽然正电子发射断层扫描（PET）可以测量心肌血流量，并描绘出高风险斑块 动脉和 CT 可以可靠地检测冠状动脉钙——动脉粥样硬化的明确标志 疾病——医生无法有效地结合这些数据来识别不良风险最高的患者 事件，由于复杂性和缺乏自动化。 至关重要的是，通过强大的、 非侵入性成像后的自动化临床工具。高效的人工智能（AI）方法是 彻底改变图像分析，并可以改进 CAD 检测和管理。总体愿景为 研究计划旨在进一步提高 PET/CT 在检测高危 CAD 方面的临床应用并指导后续治疗 通过自动化进行管理，并将所有图像和临床数据与最先进的人工智能集成。我们将建立 大型多中心 PET 和 CT 成像登记中心，具有基于图像的 AI、自动化分析和质量控制 即使在经验不足的中心也能进行可靠的分析，并综合利用所有 可用的 PET/CT 图像和临床信息（超出了主观视觉分析和临床信息所能提供的范围） 心理整合）。我们将开发人工智能对图像的直接解释，以及针对患者的特定解释 向医生提供人工智能发现。精确的定量结果将以易于理解的方式呈现给临床医生（和患者） 理解术语（例如，每年风险百分比或一种疗法与替代疗法相比的相对风险） 特定患者。这项工作将能够准确识别可以受益的高危疾病患者 先进疗法的治疗，并实现精确的患者特定风险评估和治疗 针对具有挑战性的临床情况提出的建议——患有心脏代谢疾病和晚期高危人群的 CAD 风险疾病。