Image Analysis and Machine Learning Methods for Biomarkers of Age-related and Metabolic Diseases

年龄相关疾病和代谢疾病生物标志物的图像分析和机器学习方法

基本信息

批准号：
10465018
负责人：
Sokratis Makrogiannis
金额：
$ 10.95万
依托单位：
DELAWARE STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-02 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10465018
关键词：
Abdomen Address Adipose tissue Adult Affect Age Aging Agreement Anatomy Area Atlases Baltimore Biological Markers Body Composition Bone structure Cardiovascular Diseases Classification Clinical Clinical Data Computational Technique Computer Assisted Coronary Arteriosclerosis Cross-Sectional Studies Data Data Set Descriptor Deterioration Development Diabetes Mellitus Diagnosis Diagnostic Dictionary Disease Doctor of Medicine Doctor of Philosophy Endocrinology Epidemic Epidemiology Gerontology Human Human body Image Image Analysis Imaging technology Intervention Lead Leg Link Longitudinal Studies Magnetic Resonance Imaging Measures Medical Medical Imaging Metabolic Metabolic Diseases Metabolic syndrome Methods Modernization Monitor Morphologic artifacts Morphology Muscle Noise Non-Insulin-Dependent Diabetes Mellitus Normal tissue morphology Obesity Organ Osteoporosis Participant Pathology Pattern Pennsylvania Persons Pharmacology Phenotype Physics Physiological Population Study Prevalence Principal Investigator Process Prognosis Property Quality of life Research Risk Risk Factors Shapes Skeletal Muscle Stroke Techniques Testing Therapeutic Intervention Thigh structure Time Tissue Model Tissues United States United States National Institutes of Health Visualization Work X-Ray Computed Tomography age related automated image analysis base clinical imaging deep learning disease diagnostic disorder risk epidemiology study fracture risk imaging biomarker imaging modality imaging study in vivo interest intervention effect learning strategy longitudinal analysis machine learning method mathematical methods medical schools metabolic abnormality assessment models and simulation morphometry muscle form muscle strength novel pandemic disease pre-clinical predictive test prognosis biomarker programs quantitative imaging radiological imaging sarcopenia simulation statistical and machine learning statistical learning substantia spongiosa ultrasound virtual

项目摘要

Program Director/Principal Investigator (Last, First, Middle): Makrogiannis, Sokratis, Ph.D. Abstract Age-related and metabolic diseases such as type-2 diabetes, cardiovascular diseases, and sarcopenia have become a worldwide epidemic that affects the quality of life of millions. To give a global perspective, roughly 343.8 million people in the world have type-2 diabetes today, and 175 million do not know they have diabetes at all. Metabolic diseases, such as diabetes and osteoporosis, are strongly linked to longitudinal changes in body composition, morphology and function. Modern medical imaging technologies offer the opportunity to study the composition and morphometry of human body in ways that were previously impossible. Contemporary imaging studies that are performed in vivo on a large number of participants have enabled cross-sectional and longitudinal studies of age-related and metabolic diseases, and effects of pharmacological interventions. The emergence of advanced imaging technologies has also created the need for automated image analysis techniques for identification and quantification of morphological patterns of anatomies and tissues and their changes with increasing age. This project will contribute novel and non-invasive medical image analysis techniques for studying the human body composition to achieve timely prognosis of these pathologies. Our research interests will concentrate on identification of morphological patterns in the mid-thigh, abdomen and lower leg that will eventually lead to development of imaging biomarkers. The accumulation of adipose tissue in the human body and changes of its regional distribution are associated with type-2 diabetes, cardiovascular diseases and the metabolic syndrome. Age-related changes in skeletal muscle composition are strongly linked to loss in muscle strength and mass, frequently termed as sarcopenia, leading to decreased mobility and function. Also, trabecular bone structural changes are associated with osteoporosis. We will use imaging and clinical data collected by the Baltimore Longitudinal Study of Aging (BLSA) that is the longest ongoing epidemiology study in the US. This work will address a technical and a clinical hypothesis. The technical hypothesis is that quantitative image analysis can accurately and robustly segment, register and fuse body composition data acquired by modern MRI and CT imaging scanners. The clinical hypothesis is that qualitative body composition phenotypes on clinical imaging can be used as biomarkers for prognosis and diagnosis of the metabolic syndrome manifestations. We will build on recent advances in medical image analysis to contribute novel and non-invasive techniques for studying the human body composition and its longitudinal changes with main applications in tissue identification and quantification at the mid-thigh, lower leg and the abdomen (aim 1). Then we will develop statistical machine learning methods to achieve timely diagnosis and prognosis of metabolic and age-related conditions including the metabolic syndrome and osteoporosis, and to track the effect of interventions (aim 2). OMB No. 0925-0001/0002 (Rev. 08/12 Approved Through 8/31/2015)Page Continuation Format Page

项目总监/首席研究员（最后、第一、中间）：Makrogiannis, Sokratis, Ph.D. 抽象的 2 型糖尿病、心血管疾病和肌肉减少症等与年龄相关的代谢性疾病成为影响数百万人生活质量的世界性流行病。为了提供全球视野，大致当今世界有 3.438 亿人患有 2 型糖尿病，而 1.75 亿人在当时并不知道自己患有糖尿病。全部。代谢疾病，如糖尿病和骨质疏松症，与身体的纵向变化密切相关组成、形态和功能。现代医学成像技术提供了研究物质组成和形态测量的机会以以前不可能的方式改变人体。在体内进行的当代成像研究对大量参与者的研究使得能够对与年龄相关的和代谢疾病以及药物干预的影响。先进成像技术的出现技术还产生了对自动图像分析技术的需求，用于识别和解剖学和组织的形态模式及其随年龄增长的变化的量化。该项目将为研究人类提供新颖的非侵入性医学图像分析技术身体成分以实现这些病理的及时预后。我们的研究兴趣将集中于识别大腿中部、腹部和小腿的形态模式，最终导致成像生物标志物的开发。人体内脂肪组织的积累及其变化区域分布与2型糖尿病、心血管疾病和代谢综合征有关。骨骼肌成分与年龄相关的变化与肌肉力量和质量的损失密切相关，通常被称为肌肉减少症，导致活动能力和功能下降。另外，骨小梁结构变化与骨质疏松症有关。我们将使用巴尔的摩收集的影像和临床数据老龄化纵向研究（BLSA）是美国持续时间最长的流行病学研究。这项工作将解决技术和临床假设。技术假设是定量的图像分析可以准确、稳健地分割、注册和融合通过以下方式获取的身体成分数据：现代 MRI 和 CT 成像扫描仪。临床假设是定性的身体成分表型临床影像学可作为代谢综合征预后和诊断的生物标志物表现形式。我们将利用医学图像分析的最新进展，贡献新颖且非侵入性的技术研究人体成分及其纵向变化的技术，主要应用于组织大腿中部、小腿和腹部的识别和量化（目标 1）。然后我们就开发统计机器学习方法实现代谢和年龄相关疾病的及时诊断和预后包括代谢综合征和骨质疏松症在内的疾病，并跟踪干预措施的效果（目标 2）。 OMB 编号 0925-0001/0002（修订版 08/12 批准至 8/31/2015）页面延续格式页面