Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis

右心室的深度学习评估：功能、病因和预后

• 批准号: 10756662
• 负责人: David Ouyang
• 金额: $ 24.9万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10756662
• 关键词: Address; Advisory Committees; Architecture; Arrhythmogenic Right Ventricular Dysplasia; Award; Cardiovascular Diseases; Cardiovascular system; Categories; Cause of Death; Cessation of life; Characteristics; Classification; Clinical; Communities; Computer Vision Systems; Coronary Arteriosclerosis; Data; Data Science; Data Set; Derivation procedure; Diagnosis; Disease; Disease Outcome; Dissection; Early Diagnosis; Echocardiography; Engineering; Epidemiology; Etiology; Evaluation; Framingham Heart Study; Future; Goals; Health system; Healthcare; Healthcare Systems; Heart failure; Heterogeneity; Hospitals; Human; Image; Incidence; Learning; Machine Learning; Maps; Measures; Medical Imaging; Medicine; Mentors; Modeling; Morbidity - disease rate; Morphology; Motion; Multi-Ethnic Study of Atherosclerosis; Nature; Outcome; Participant; Patients; Pattern; Performance; Phase; Phenotype; Physicians; Prognosis; Program Development; Prospective cohort; Pulmonary Embolism; Research; Research Personnel; Right Ventricular Dysfunction; Right Ventricular Function; Right ventricular structure; Risk; Risk Factors; Sampling; Scientist; Semantics; Standardization; Stroke; Techniques; Training; Translational Research; United States; Validation; Variant; Ventricular; cardiovascular imaging; cardiovascular risk factor; career; career development; cohort; community based care; computer science; cost; deep learning; deep learning algorithm; deep learning model; deep neural network; design; hands on research; healthcare community; heart imaging; image translation; imaging science; improved; mortality; neural network; neural network architecture; novel; outcome prediction; performance tests; population based; predictive modeling; prognostic; programs; public health relevance; pulmonary arterial hypertension; skills; spatiotemporal; statistics; study population; success; temporal measurement

ABSTRACT Heart failure imposes a tremendous burden of morbidity and mortality, costing the United States in excess of $31 billion annually. An increasingly recognized major determinant of outcomes in heart failure is right ventricular (RV) dysfunction. However, the nature and character of RV contribution to cardiovascular outcomes remains poorly understood, largely due to the imprecision of imaging and interpretation of RV morphology and function. Echocardiography, with its high temporal resolution and low cost of acquisition, serves as frontline cardiovascular imaging and a mainstay in approaches to assessing RV morphology and function. However, echocardiographic imaging of the RV is limited by factors that include technical variation in image acquisition and heterogeneity in image assessment as well as overall interpretation. We postulate that deep learning based phenotyping can offer the ability to not only more precisely characterize RV function but also classify RV imaging phenotypes according to etiologic disease states and, even further, refine prognostic evaluations of future cardiovascular risk. Therefore, in Aim 1, we will use video-based deep learning segmentation models to assess RV function, evaluate its cross-sectional relation with a range of expert-measured parameters, and examine its variation in the context of patient characteristics derived from large hospital-based cohorts. In Aim 2, we will use video-based deep learning models to produce imaging-based classification of RV disease and assess the ability of unsupervised approaches to classify RV dysfunction into various categories of disease etiology. In Aim 3, we will use models developed in part from training in Aims 1 and 2 to predict major cardiovascular outcomes including heart failure in addition to coronary artery disease, stroke, and cardiovascular death in both hospital- based and community-based cohorts. The overarching goal of this proposal is to improve the precision and standardization of RV phenotyping and determine the extent to which deep learning models can augment human assessment of the RV. This research will be accomplished in the setting of a comprehensive career development program designed to provide the candidate with the skills needed to become an independent physician-scientist in cardiovascular medicine and translational imaging science. An advisory committee of established scientists/mentors in the fields of cardiac imaging, deep learning, data science, and translational science will guide the candidate in his transition to scientific independence over the course of the award period.

摘要 心力衰竭造成了巨大的发病率和死亡率负担，使美国的成本超过2000万美元。 每年310亿美元。越来越多的人认识到心力衰竭结局的主要决定因素是右心室 (RV)功能障碍然而，RV对心血管结局的贡献的性质和特征仍然存在， 了解甚少，主要是由于成像和RV形态和功能解释的不精确。 超声心动图具有时间分辨率高、采集成本低的特点， 影像学和主要方法来评估RV形态和功能。然而，超声心动图 RV的成像受到多种因素的限制，这些因素包括图像采集中的技术变化和 图像评估以及总体解释。我们假设基于深度学习的表型可以 不仅能够更精确地表征RV功能，而且能够对RV成像表型进行分类 根据病因疾病状态，甚至进一步，完善未来心血管疾病的预后评估， 风险因此，在目标1中，我们将使用基于视频的深度学习分割模型来评估RV功能， 评估其与一系列专家测量参数的横截面关系，并检查其在 来自大型医院队列的患者特征背景。在目标2中，我们将使用基于视频的 深度学习模型，以产生基于成像的RV疾病分类，并评估 将RV功能障碍分类为各种疾病病因的无监督方法。在目标3中，我们 我将使用目标1和目标2中部分培训开发的模型来预测主要的心血管结局 包括心力衰竭以及冠状动脉疾病、中风和心血管死亡， 以社区为基础的群体。该提案的总体目标是提高精确度， RV表型的标准化，并确定深度学习模型可以在多大程度上增强人类 对RV的评价本研究将在全面职业发展的背景下完成 旨在为候选人提供成为独立的物理学家-科学家所需的技能的计划 心血管医学和转化成像科学。成立咨询委员会， 心脏成像、深度学习、数据科学和转化科学领域的科学家/导师将 指导候选人在获奖期间向科学独立过渡。