Spatiotemporal and Deep Learning Analysis of Cardiac Imaging for Predictive Risk Stratification in Duchenne Muscular Dystrophy

心脏成像的时空和深度学习分析用于杜氏肌营养不良症的预测风险分层

• 批准号: 10536912
• 负责人: Conner Earl
• 金额: $ 5.43万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2026-08-04
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10536912
• 关键词: 3-Dimensional; 4D Imaging; Address; Affect; Age of Onset; Area; Artificial Intelligence; Award; Biological Markers; Biomechanics; Biomedical Engineering; Birth; Cardiac; Cardiomyopathies; Cause of Death; Childhood; Clinical; Clinical Research; Clinical Treatment; Clinical Trials; Collaborations; Computer Analysis; Data; Data Science; Data Set; Databases; Diagnostic; Disease; Disease Progression; Doctor of Philosophy; Duchenne cardiomyopathy; Duchenne muscular dystrophy; Early Diagnosis; Early identification; Early treatment; Engineering; Evaluation; Fellowship; Future; Genetic Diseases; Heart; Heart Diseases; Image; Image Analysis; Incidence; Indiana; Individual; Institution; Kinetics; Left; Length; Link; Longevity; Maps; Measures; Medical; Mentorship; Methods; Muscle Weakness; National Heart, Lung, and Blood Institute; Neuromuscular Diseases; Onset of illness; Outcome Measure; Patient-Focused Outcomes; Patients; Pattern; Pediatric Hospitals; Pediatric cardiology; Phenotype; Physicians; Physiology; Play; Population; Population Study; Prognosis; Protocols documentation; Quality of life; Registries; Research; Research Training; Risk; Role; Scientist; Standardization; Symptoms; Techniques; Teenagers; Time; Training; Universities; Ventricular; Vulnerable Populations; Work; algorithm development; automated analysis; automated segmentation; biomarker development; boys; cardiac magnetic resonance imaging; cardiovascular imaging; clinical center; convolutional neural network; data registry; deep learning; deep learning algorithm; deep neural network; early onset; frontier; heart function; heart imaging; imaging biomarker; imaging facilities; imaging modality; improved; improved outcome; innovation; insight; kinematics; male; medical schools; mortality; novel; novel diagnostics; novel therapeutic intervention; patient population; patient stratification; pediatric cardiologist; predictive marker; prevent; repository; risk stratification; spatiotemporal; treatment response

PROJECT SUMMARY/ABSTRACT Heart disease is the leading cause of death for individuals with Duchenne muscular dystrophy (DMD). DMD is a devastating and progressive neuromuscular disease with no known cure. This X-linked genetic disorder affects nearly 1 in 5000 boys and manifests as debilitating muscle weakness and progressive cardiomyopathy (CM). While CM in some individuals with DMD progresses rapidly and fatally in their teenage years, others can live relatively symptom-free into their thirties or forties. Early identification and treatment can improve quality and length of life, but currently, there are no standard imaging biomarkers that can detect early onset or rapidly progressing DMD CM. Additionally, research in this area has lagged due to small population study sizes and limited standardized imaging data. To that end, this project will utilize the largest standardized imaging data registry of DMD CM created through the collaboration of 6 of the largest medical institutions with DMD CM expertise. Following the objective set up by the National Heart, Lung, and Blood Institute (NHLBI) to “develop and optimize novel diagnostic and therapeutic strategies to prevent, treat, and cure HLBS diseases” we propose the following Aim: 1) Identify imaging biomarkers of DMD CM onset and progression using novel image analysis. Utilizing recently developed methods of spatiotemporal analysis of 4D (3D plus time) cardiac imaging data, we can evaluate localized kinematic parameters in the heart that may be sensitive to subtle changes in disease physiology. With this project we also follow a second major objective set forth by NHBLI to “Leverage emerging opportunities in data science to open new frontiers in HLBS research” through another Aim: 2) Apply deep learning neural network to DMD registry to evaluate CM onset and progression. Utilizing the large DMD CM imaging registry, we will apply deep learning techniques for automated segmentation and analysis of cardiac parameters to evaluate patterns of early-onset and rapid progression. These results will help to bridge a crucial gap in optimizing clinical treatment for a devastating pediatric disease and pave the way for future research and innovation through the definition of robust imaging This fellowship research and training will be carried out at Purdue University under the direct mentorship of Craig Goergen, PhD who is a leading expert in cardiovascular imaging and biomechanics research and at Indiana University School of Medicine with Larry Markham, MD, Division Chief of Pediatric Cardiology and renowned physician scientist with a focus on DMD CM. Guang Lin, PhD (Purdue University), a data science expert, will provide expertise in the deep learning algorithm development. Han Kor, MD (Nationwide Children’s Hospital), May Ling Mah, MD (Nationwide Children’s Hospital), and Jonathan Soslow, MD (Vanderbilt School of Medicine) are all practicing pediatric cardiologists with expertise in DMD cardiac imaging and will provide access to data, clinical insight, training, and mentorship for this project. biomarkers and clinical trial endpoints.

项目概要/摘要 心脏病是杜氏肌营养不良症 (DMD) 患者死亡的主要原因。 一种毁灭性的进行性神经肌肉疾病，目前尚无已知的治愈方法。 影响近五千分之一的男孩，表现为衰弱性肌肉无力和进行性心肌病 (CM) 虽然某些 DMD 患者的 CM 在青少年时期进展迅速且致命，但其他人则可能会出现这种情况。 活到三四十岁相对无症状。早期识别和治疗可以提高质量。 和生命长度，但目前还没有标准的成像生物标志物可以检测早期发病或快速发病 此外，由于研究规模较小，该领域的研究已经滞后。 为此，该项目将利用最大的标准化成像数据。 6家最大的医疗机构与DMD CM合作创建的DMD CM登记册 遵循国家心肺血液研究所 (NHLBI) 制定的目标 “开发和优化新的诊断和治疗策略来预防、治疗和治愈 HLBS 1) 识别 DMD CM 发病和进展的影像生物标志物 利用新开发的 4D 时空分析方法（3D plus）。 时间）心脏成像数据，我们可以评估心脏中可能敏感的局部运动参数 通过这个项目，我们还遵循了第二个主要目标。 NHBLI 将“利用数据科学领域的新兴机遇，开辟 HLBS 研究的新领域” 通过另一个目标：2）将深度学习神经网络应用于 DMD 注册表以评估 CM 发作和 利用大型 DMD CM 成像注册中心，我们将应用深度学习技术 心脏参数的自动分割和分析，以评估早发和快速发作的模式 这些结果将有助于弥合优化临床治疗以应对毁灭性灾难的关键差距。 儿科疾病，通过稳健成像的定义为未来的研究和创新铺平道路 该奖学金研究和培训将在普渡大学进行 大学由心血管领域专家 Craig Goergen 博士直接指导 成像和生物力学研究，在印第安纳大学医学院与 Larry Markham 医学博士合作， 儿科心脏病科主任、专注于 DMD CM 的著名医师科学家。 博士（普渡大学），数据科学专家，将提供深度学习算法方面的专业知识 Han Kor，医学博士（全国儿童医院），May Ling Mah，医学博士（全国儿童医院） 医院）和乔纳森·索斯洛医学博士（范德比尔特医学院）都是执业儿科心脏病专家 拥有 DMD 心脏成像方面的专业知识，并将提供数据、临床见解、培训和指导 对于这个项目。 生物标志物和临床试验终点。