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: 10601119
• 负责人: Piotr J Slomka
• 金额: $ 100.92万
• 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/10601119
• 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 Imaging; Functional disorder; Heart; High Prevalence; Image; Image Analysis; Inflammation; Measures; Modality; 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; adverse event risk; aggressive therapy; artificial intelligence method; automated analysis; coronary artery calcium; disorder risk; experience; high risk; high risk population; imaging modality; improved; molecular imaging; non-invasive imaging; novel; obese patients; outcome prediction; programs; structural imaging; 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.

项目摘要 冠状动脉疾病（CAD）仍然是一个主要的公共卫生问题，在美国的患病率很高 人口功能，分子和结构成像提供了一个独特的机会，了解 CAD的病理生理学，特别是在高风险人群中，如肥胖、糖尿病和慢性 肾脏疾病（心脏代谢疾病）。通过成像进行CAD评价是基于评估（1） 心肌缺血和心肌血流量（2）动脉粥样硬化的解剖学负荷，以及（3）疾病活动性 使用新的技术。 然而，医生还不能最佳地使用这些数据来识别不良反应风险最高的患者。 事件-由于先进的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，并描绘高危斑块， 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物 疾病-医生无法有效地将这些数据联合收割机以识别不良反应风险最高的患者 事件，由于复杂性和缺乏自动化。 重要的是，存在对通过鲁棒的， 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 革命性的图像分析，可以提高CAD检测和管理。2000年的总体愿景 研究计划是进一步提高PET/CT在检测高风险CAD和指导后续治疗中的临床应用 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AI集成。我们将建立 大型多中心PET和CT成像注册中心，以及基于图像的AI、自动分析和质量控制 即使在经验不足的中心，也可以进行可靠的分析，并开发决策支持工具， 可用的PET/CT图像和临床信息（超出主观视觉分析的可能性， 心理整合）。我们将开发人工智能对图像的直接解释，以及针对患者的解释 AI发现给医生。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，%风险/年或作为一种治疗与替代治疗相比的相对风险）， 具体病人。这项工作将允许准确识别患有高危疾病的患者， 从先进的治疗，并实现精确的患者特定的风险估计和治疗 在挑战临床疑难杂症的建议-在CAD与心脏代谢疾病和先进的高- 风险疾病。

项目成果

Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation.

人工智能模型预测心脏骤停表现为无脉电活动与心室颤动。

• DOI: 10.1161/circep.123.012338
• 发表时间: 2024
• 期刊: Circulation. Arrhythmia and electrophysiology
• 作者: Holmstrom,Lauri;Bednarski,Bryan;Chugh,Harpriya;Aziz,Habiba;Pham,HoangNhat;Sargsyan,Arayik;Uy-Evanado,Audrey;Dey,Damini;Salvucci,Angelo;Jui,Jonathan;Reinier,Kyndaron;Slomka,PiotrJ;Chugh,SumeetS
• 通讯作者: Chugh,SumeetS

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

• DOI: 10.1007/s00259-023-06323-z
• 发表时间: 2023-10
• 期刊: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
• 影响因子: 9.1
• 作者: Kuronuma, Keiichiro;Miller, Robert J. H.;Van Kriekinge, Serge D.;Han, Donghee;Singh, Ananya;Gransar, Heidi;Dey, Damini;Berman, Daniel S.;Slomka, Piotr J.
• 通讯作者: Slomka, Piotr J.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging.

比较接受 SPECT 心肌灌注成像的患者中衰减 CT 冠状动脉钙化的量化和视觉估计的预后价值。

• DOI: 10.1007/s10554-023-02980-1
• 发表时间: 2024
• 期刊: The international journal of cardiovascular imaging
• 作者: Feher,Attila;Pieszko,Konrad;Shanbhag,Aakash;Lemley,Mark;Miller,RobertJh;Huang,Cathleen;Miras,Leonidas;Liu,Yi-Hwa;Gerber,Jamie;Sinusas,AlbertJ;Miller,EdwardJ;Slomka,PiotrJ
• 通讯作者: Slomka,PiotrJ