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A photographic method for human body composition assessment

一种用于人体成分评估的摄影方法

基本信息

批准号：
8835138
负责人：
Helen Olivia Affuso
金额：
$ 51.54万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-06-01 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8835138
关键词：
Abbreviations Advisory Committees Age Aging Air Algorithms Alzheimer&apos s Disease Anthropometry Body Composition Body Size Body Weight Body fat Body mass index Cachexia Categories Child Clinical Clinical Research Computational algorithm Data Devices Disease Dual-Energy X-Ray Absorptiometry Eating Disorders Epidemiologic Studies Equipment Exercise Fatty acid glycerol esters Growth HIV Hand Health Height Home environment Human Human body Image Image Analysis Imagery Individual Laboratories Laboratory Study Lead Longevity Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Measurement Measures Methods Multiple Sclerosis Muscular Dystrophies Obesity Painless Parkinson Disease Participant Persons Plethysmography Population Public Health Race Radiation Relative (related person)Research Sampling Skeletal Muscle Skin Strategic Planning System Techniques Technology Testing Time Training United States National Institutes of Health Validation Visual Weight Woman Work Writing X-Ray Computed Tomography cost digital digital imaging electric impedance experience field study frailty improved interest lean body mass light weight men muscle form nervous system disorder novel portability primary outcome response sarcopenia sex waist circumference wasting

项目摘要

DESCRIPTION (provided by applicant): The assessment of body composition, particularly fat and fat free mass, is vital to understanding many health-related conditions including obesity and sarcopenia, whose very definitions depend on assessment of fat and fat free mass, but also cachexia induced by HIV, cancer, and other diseases; multiple sclerosis; wasting in neurological disorders such as Parkinson's, Alzheimer's, muscular dystrophy; eating disorders; proper growth in children; and yet others still. Nevertheless, challenges remain in measuring body composition. Existing methods beyond height, weight, and very simple anthropometry are still not the norm in large-scale epidemiologic studies and clinical studies. This is because of concerns, depending on method, about cost, portability, time, radiation exposure, and accuracy. Calculation of body mass index (BMI; kg/m2) is a commonly used alternative, but is limited in that it is an assessment of body weight relative to height and not of body fatness per se. A hand-held device that could be carried by any individual, accurately assess fat mass and skeletal muscle mass, and be utilized without any discomfort to the subject, inexpensively, and without radiation exposure would be highly desirable. Evidence suggests that highly experienced trained observers can accurately estimate percentage of body fat by visual examination of subjects, but concerns about accuracy and subjectivity predominate. We hypothesize that we can develop a computer algorithm which can perform as or more accurately when analyzing photographic imagery data, will require no repeated retraining, and eliminate subjectivity. Our first specific aim is to develop a computer image analysis algorithm to accurately estimate adiposity and skeletal muscle mass from standardized photographic images in a bi-ethnic sample of men, women, and children. Our second specific aim is to validate the algorithm in a large diverse sample of men, women, and children.

描述(申请人提供)：对身体成分，尤其是脱脂质量的评估，对于了解许多与健康相关的疾病至关重要，包括肥胖和石棺减少，其定义取决于对脂肪及脱脂质量的评估，但也包括由艾滋病毒、癌症和其他疾病引起的恶病质；多发性硬化症；在神经疾病中的消瘦，如帕金森氏症、阿尔茨海默氏症、肌营养不良；饮食障碍；儿童的正常发育；以及其他。然而，在测量身体成分方面仍然存在挑战。除了身高、体重和非常简单的人体测量之外，现有的方法仍然不是大规模流行病学研究和临床研究的标准。这是因为根据方法的不同，对成本、便携性、时间、辐射暴露和准确性的担忧。计算体重指数(BMI；kg/m2)是一种常用的替代方法，但它的局限性在于它是相对于身高的体重评估，而不是身体脂肪本身的评估。任何人都可以携带的手持设备，可以准确地评估脂肪质量和骨骼肌质量，并且可以在不对受试者造成任何不适的情况下使用，价格低廉，而且不会受到辐射照射，这将是非常理想的。有证据表明，经验丰富的训练有素的观察者可以通过对受试者的肉眼检查来准确地估计身体脂肪的百分比，但对准确性和主观性的担忧占主导地位。我们假设我们可以开发出一种计算机算法，该算法在分析摄影图像数据时可以执行得像或更准确，不需要重复训练，并消除了主观性。我们的第一个具体目标是开发一种计算机图像分析算法，从标准化的摄影图像中准确地估计男性、女性和儿童的双民族样本中的肥胖症和骨骼肌质量。我们的第二个具体目标是在大量不同的男性、女性和儿童样本中验证算法。