Deep Learning To Automate Late Mechanical Activation Detection From Cardiac Magnetic Resonance Images

深度学习自动检测心脏磁共振图像的晚期机械激活

• 批准号: 10593788
• 负责人: Miaomiao Zhang
• 金额: $ 22.37万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593788
• 关键词: Address; Adoption; Agreement; Arrhythmia; Artificial Intelligence; Brain natriuretic peptide; Cardiac; Characteristics; Cicatrix; Computer software; Computers; Consumption; Data; Data Set; Defibrillators; Detection; Devices; Dimensions; Disease; Echocardiography; Educational process of instructing; Effectiveness; Electronics; Exercise Test; Gadolinium; Goals; Grant; Heart; Heart failure; Image; Implant; Inferior; Institution; Joints; Knowledge; Lead; Left; Life; Location; Longterm Follow-up; Machine Learning; Magnetic Resonance; Maps; Measures; Mechanics; Methods; Modeling; Morbidity - disease rate; Myocardial; Network-based; Outcome; Pacemakers; Patient Selection; Patients; Performance; Procedures; Quality of life; Questionnaires; Selection for Treatments; Serum; Site; Specialist; Techniques; Testing; Time; Tissue Viability; Ventricular; Visual; Work; artificial intelligence method; cardiac implant; cardiac magnetic resonance imaging; cardiac resynchronization therapy; comorbidity; deep learning; deep neural network; demographics; design; experience; follow-up; functional restoration; heart function; heart rhythm; imaging study; implantable device; improved; innovation; machine learning method; mortality; multi-task learning; neural network; novel; novel strategies; response; therapy outcome; tool; treatment response

Project summary: This proposal aims to develop advanced machine learning and artificial intelligence (ML/AI) techniques to rapidly and accurately identify sites with late mechanical activation (LMA) and compute circumferential uniformity estimate with singular value decomposition (CURE-SVD) from standard cine cardiac magnetic resonance (CMR) images. Our long-term goal is to develop networks that can determine LMA sites / CURE-SVD automatically from cine images acquired at any CMR facility worldwide, thereby addressing a critical need in the effective guidance of device- based therapies, such as Cardiac resynchronization therapy (CRT), for potentially millions of heart failure patients. To accomplish this goal, we will make use of a rich and unique dataset we have assembled at our institution based on over 200 patients undergoing CRT with a median follow-up of five years. The data set includes demographics and comorbid diseases from EHR review, pre- CRT/post-CRT imaging with CMR cine/DENSE/LGE (late gadolinium enhancement), echocardiography, and multidimensional response parameters based on overall survival, serum B-type natriuretic peptide testing, quality of life questionnaires, and exercise testing for peak VO2. The central hypothesis of this proposal is that these ML/AI methods will effectively identify the characteristics of scar-free LMA sites from cine imaging, achieving excellent agreement compared with the original DENSE-based assessments, and predict post-CRT outcomes. Our specific aims are (i) identifying LMA sites and computing CURE-SVD by developing joint neural networks with inputs from cine SSFP/GRE images, (ii) with the addition of scar from LGE in the network, we will develop a novel multi-task learning to consider scar information in the determination of LMA sites free of scar, and (iii) comparing the performance of our proposed methods with ground truth DENSE and results obtained from commercial feature tracking software to predict CRT outcomes in the dataset with 200+ CRT patients with complete CRT response data and long-term follow-up for survival and arrhythmia outcomes.

项目概要： 该提案旨在开发先进的机器学习和人工智能（ML/AI） 快速准确识别晚期机械激活（LMA）部位的技术， 用奇异值分解（CURE-SVD）计算圆周均匀性估计， 标准电影心脏磁共振（CMR）图像。我们的长期目标是发展 网络，可根据采集的电影图像自动确定LMA部位/ CURE-SVD， 全球任何CMR设施，从而满足有效引导设备的关键需求- 基于治疗，如心脏起搏治疗（CRT），为潜在的数百万心脏 失败的病人为了实现这一目标，我们将利用我们拥有的丰富而独特的数据集 在我们的机构收集了超过200例接受CRT的患者，中位随访时间为 五年数据集包括来自EHR回顾的人口统计学和共病疾病， CRT/CRT后成像，CMR电影/DENSE/LGE（晚期钆增强）， 超声心动图和基于总生存期、血清 B型利钠肽检测、生活质量问卷调查和运动试验的峰值VO 2。 该提案的中心假设是，这些ML/AI方法将有效地识别 电影成像中无瘢痕LMA部位的特征，达到极好的一致性 与最初的基于DENSE的评估相比，并预测CRT后的结果。我们 具体目标是（i）通过开发联合神经网络来识别LMA站点并计算CURE-SVD 网络与输入从电影SSFP/GRE图像，（ii）与添加疤痕从LGE在 网络，我们将开发一种新的多任务学习，考虑疤痕信息， 确定LMA网站无疤痕，和（iii）比较我们提出的性能 方法与地面真理密度和商业特征跟踪获得的结果 预测200多例接受完全CRT的CRT患者数据集中CRT结局的软件 反应数据和长期随访的生存率和心律失常的结果。