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） 快速准确地识别晚期机械激活（LMA）位点的技术 使用奇异值分解 (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 可以根据在以下位置获取的电影图像自动确定 LMA 站点/CURE-SVD 的网络 世界各地的任何 CMR 设施，从而满足有效指导设备的关键需求 基础疗法，例如心脏再同步疗法 (CRT)，可治疗数以百万计的心脏患者 失败患者。为了实现这一目标，我们将利用我们拥有的丰富且独特的数据集 我们机构根据 200 多名接受 CRT 的患者进行汇总，并进行中位随访 五年。该数据集包括来自 EHR 审查、预审的人口统计数据和共病疾病 CRT/CRT 后成像，采用 CMR cine/DENSE/LGE（后期钆增强）， 超声心动图和基于总生存率、血清的多维反应参数 B 型利钠肽测试、生活质量问卷和峰值摄氧量运动测试。 该提案的中心假设是这些 ML/AI 方法将有效地识别 电影成像中无疤痕 LMA 部位的特征，实现了极佳的一致性 与最初基于 DENSE 的评估进行比较，并预测 CRT 后的结果。我们的 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 网络的输入来自电影 SSFP/GRE 图像，(ii) 在 网络中，我们将开发一种新颖的多任务学习来考虑疤痕信息 确定无疤痕的 LMA 部位，以及 (iii) 比较我们提出的性能 具有地面实况 DENSE 的方法和从商业特征跟踪获得的结果 用于预测数据集中 200 多名具有完整 CRT 的 CRT 患者的 CRT 结果的软件 反应数据以及生存和心律失常结果的长期随访。