Machine Learning-based Imaging Biomarkers for Metabolic and Age-related Diseases

基于机器学习的代谢和年龄相关疾病的成像生物标志物

基本信息

批准号：
10707354
负责人：
Sokratis Makrogiannis
金额：
$ 33.11万
依托单位：
DELAWARE STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-20 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10707354
关键词：
3-Dimensional Abdomen Acceleration Address Adipose tissue Adult Advanced Development Affect Age Aging Agreement Anatomy Architecture Area Artificial Intelligence Atlases Baltimore Biological Black race Body Composition Body Regions Bone Density Bone structure Cardiovascular Diseases Classification Clinical Data Clinical Trials Communities Computer Assisted Computer software Coronary Arteriosclerosis Data Data Set Degenerative polyarthritis Descriptor Deterioration Development Diabetes Mellitus Diagnosis Diagnostic Dictionary Disease Epidemic Female Health Care Costs Hispanic Human Human body Image Image Analysis Imaging technology Infiltration Intervention Joints Link Longitudinal Studies Lower Extremity Machine Learning Magnetic Resonance Imaging Manuals Measures Medical Imaging Metabolic Metabolic Diseases Metabolic dysfunction Metabolic syndrome Methods Microscopic Minority Access Minority Groups Modernization Monitor Morphology Muscle Non-Insulin-Dependent Diabetes Mellitus Not Hispanic or Latino Obesity Osteoporosis Participant Pathologist Pathology Pattern Persons Phenotype Physiological Prevalence Principal Investigator Process Prognosis Property Quality of life Reader Research Risk Risk Factors Risk Reduction Skeletal Muscle Software Tools Source Code Stroke Techniques Testing Therapeutic Time Tissues Training Underserved Population United States Visualization Work X-Ray Computed Tomography age related automated image analysis bone clinical decision-making clinical imaging cost effective deep learning design diabetes control disease diagnosis epidemiology study ethnic minority health care quality health equity imaging biomarker imaging study improved in vivo innovation interest learning strategy machine learning method male mathematical methods metabolic rate morphometry muscle form muscle strength novel open source pharmacologic preservation programs quantitative imaging racial minority radiological imaging radiologist radiomics research and development sarcopenia statistical learning substantia spongiosa

项目摘要

Machine Learning-based Imaging Biomarkers for Metabolic and Age-related Diseases Specific Aims Age-related and metabolic diseases such as type-2 diabetes mellitus (T2DM), cardiovascular disease (CVD), obesity, osteoporosis 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 obesity, are strongly linked to longitudinal changes in body composition, morphology and function. 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. The accumulation of adipose tissue in the human body and changes of its regional distribution are associated with type-2 diabetes, cardiovascular disease and the metabolic syndrome. 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 diagnosis of these pathologies. Our research interests will concentrate on identification of morphological patterns in the lower extremity that will eventually lead to development of imaging biomarkers. 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, as well as publicly available datasets. 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 changes (aim 1). Then we will develop machine learning methods for timely diagnosis and prognosis of metabolic and age-related diseases (aim 2). We will implement these techniques as open-source software for further use and development by the research community (aim 3).

基于机器学习的成像生物标志物，用于代谢和年龄有关疾病具体目标与年龄相关的和代谢性疾病，例如2型糖尿病（T2DM），心血管疾病（CVD），肥胖，骨质疏松症和肌肉减少症已成为全球流行病会影响数百万人的生活质量。给出全球视角，大约343.8 当今世界上百万人患有2型糖尿病，1.75亿人根本不知道他们有糖尿病。代谢疾病，例如糖尿病和肥胖，与身体成分，形态和形态的纵向变化密切相关功能。骨骼肌组成的变化与肌肉力量和质量的损失密切相关，经常被称为肌肉减少症，导致迁移率和功能降低。脂肪组织在人体中的积累其区域分布的变化与2型糖尿病，心血管疾病和代谢有关综合征。在大量参与者的体内进行的现代成像研究已启用了十字架与年龄相关和代谢疾病的分段和纵向研究以及药理学干预的作用。高级成像技术的出现还创造了对自动图像分析技术的需求鉴定和定量解剖和组织的形态模式及其随着增加的变化年龄。该项目将为研究人类提供新颖和非侵入性医学图像分析技术身体成分以及时诊断这些病理。我们的研究兴趣将集中在下肢中形态学模式的识别，最终会导致成像的发展生物标志物。我们将使用巴尔的摩纵向研究（BLSA）收集的成像和临床数据，是美国正在进行的流行病学研究，以及公开可用的数据集。我们将建立在最近的基础上医学图像分析的进步，贡献了研究人体的新颖和非侵入性技术组成及其变化（AIM 1）。然后，我们将开发机器学习方法以及时诊断和预后代谢和与年龄有关的疾病（AIM 2）。我们将将这些技术作为开源软件实施研究界的使用和开发（AIM 3）。