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: 10833464
• 负责人: Conner Earl
• 金额: $ 3.52万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10833464
• 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; Medicine; 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; Predisposition; Prognosis; Protocols documentation; Quality of life; Registries; Research; 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 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 prediction; risk stratification; skills; 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.

项目摘要/摘要 心脏病是Duchenne肌肉营养不良（DMD）患者死亡的主要原因。 DMD是 无法治愈的毁灭性和进行性神经肌肉疾病。这种X连锁遗传疾病 影响近5000名男孩中的近1个，表现为使肌肉无力和进行性心肌病变 （厘米）。虽然在某些DMD的人中，CM在十几岁的时候就迅速而致命地进展 在三十多岁或四十年代中相对无症状。早期识别和治疗可以提高质量 和寿命的长度，但目前，没有标准成像生物标志物可以检测到早期或快速发作 进展DMD CM。此外，由于人口研究的规模较小，该领域的研究滞后于 有限的标准成像数据。为此，该项目将利用最大的标准化成像数据 DMD CM注册表通过6个最大的医疗机构与DMD CM的合作创建 专家。遵循国家心脏，肺和血液研究所（NHLBI）建立的目标 “开发和优化新颖的诊断和治疗策略来预防，治疗和治愈HLB 疾病”我们提出以下目的：1）识别DMD CM发作和进展的成像生物标志物 使用新颖的图像分析。利用最近开发的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学参数 疾病生理学的细微变化。在这个项目中，我们还遵循第二个主要目标 NHBLI“利用数据科学的新兴机会来开放HLBS研究的新领域” 通过另一个目的：2）将深度学习神经网络应用于DMD注册表以评估CM发作和 进展。利用大型DMD CM成像注册表，我们将应用深度学习技术 对心脏参数的自动分割和分析，以评估早期和快速的模式 进展。这些结果将有助于弥合至关重要的差距，以优化造成破坏性的临床治疗 小儿疾病，为未来的研究和创新铺平了道路，通过鲁棒成像的定义 该奖学金研究和培训将在普渡大学进行 大学以Craig Gorgen的直接心态，博士学位，他是心血管领域的领先专家 成像和生物力学研究以及印第安纳大学医学院与马里兰州拉里·马克汉姆（Larry Markham） 小儿心脏病学和著名的物理科学家部门负责人，重点是DMD CM。广林， 数据科学专家（Purdue University）将提供深度学习算法的专业知识 发展。医学博士Han Kor（全国儿童医院），May Ling Mah，医学博士（全国儿童医院）。 医院）和马里兰州乔纳森·索斯洛（Jonathan Soslow）（范德比尔特医学院）都在执业儿科心脏病专家 具有DMD心脏成像方面的专业知识，并将提供数据，临床见解，培训和精神船的访问权限 对于这个项目。 生物标志物和临床试验终点。