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

项目总结/摘要 心脏病是杜氏肌营养不良症（DMD）患者死亡的主要原因。DMD是 一种毁灭性的、进行性的神经肌肉疾病，目前尚无治愈方法。这种X染色体连锁遗传病 每5000个男孩中就有1个发病，表现为肌无力和进行性心肌病 （CM）。虽然CM在一些DMD患者的青少年时期迅速发展并致命，但其他人可以 在三四十岁时生活相对自由。早期识别和治疗可以提高质量 和寿命，但目前，没有标准的成像生物标志物，可以检测早期发作或快速 进行DMD CM。此外，由于人口研究规模小， 有限的标准化成像数据。为此，该项目将利用最大的标准化成像数据， 通过与DMD CM合作的6家最大的医疗机构创建的DMD CM注册表 专业知识根据国家心脏、肺和血液研究所（NHLBI）制定的目标， “开发和优化新的诊断和治疗策略，以预防，治疗和治愈HLBS 我们提出以下目的：1）鉴定DMD CM发病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（3D+）时空分析方法， 时间）心脏成像数据，我们可以评估心脏中的局部运动学参数， 疾病生理学的细微变化。在这个项目中，我们还遵循了第二个主要目标， NHBLI将“利用数据科学中的新兴机会，开辟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登记，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， 自动分割和分析心脏参数，以评估早发性和快速 进展这些结果将有助于弥合一个关键的差距，优化临床治疗的破坏性 儿科疾病，并通过定义强大的成像为未来的研究和创新铺平道路 这项奖学金研究和培训将在普渡大学进行 大学在克雷格Goergen博士的直接指导下，他是心血管领域的领先专家 成像和生物力学研究，并在印第安纳州大学医学院与拉里马卡姆，医学博士， 儿科心脏病科主任和著名的内科医生科学家，专注于DMD CM。林广， 博士（普渡大学），数据科学专家，将提供深度学习算法的专业知识 发展Han Kor，MD（全国儿童医院），May Ling Mah，MD（全国儿童医院） 医院）和Jonathan Soslow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，并将提供数据访问，临床见解，培训和指导 为这个项目。 生物标志物和临床试验终点。

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.