A photographic method for human body composition assessment

一种用于人体成分评估的摄影方法

• 批准号: 8295495
• 负责人: Helen Olivia Affuso
• 金额: $ 50.47万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8295495
• 关键词: Abbreviations; Advisory Committees; Age; Aging; Air; Algorithms; Alzheimer' 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; light weight; men; muscle form; nervous system disorder; novel; portability; primary outcome; response; sarcopenia; sex; waist circumference; wasting

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. PUBLIC HEALTH RELEVANCE: Obesity, loss of skeletal muscle with aging, and other body composition alterations are of profound public health importance. We propose a novel method for measuring the quantity of fat and skeletal muscle mass in humans quickly, painlessly, safely, accurately, and inexpensively that can subsequently be used in many large scale and remote studies.

描述（由申请人提供）：评估身体成分，特别是脂肪和无脂肪量，对于了解许多与健康相关的疾病至关重要，包括肥胖和肌肉减少症，其定义取决于对脂肪和无脂肪量的评估，但也取决于艾滋病、癌症和其他疾病引起的恶病质；多发性硬化;帕金森氏症、阿尔茨海默氏症、肌肉萎缩症等神经系统疾病的浪费；饮食失调;儿童的正常成长；还有一些人。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，根据不同的方法，需要考虑成本、便携性、时间、辐射暴露和准确性。计算身体质量指数（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，而不是身体肥胖本身。一种任何人都可以携带的手持设备，准确地评估脂肪量和骨骼肌量，并且在使用时不会给受试者带来任何不适，价格低廉，没有辐射暴露，这是非常可取的。有证据表明，经验丰富、训练有素的观察者可以通过目视检查准确地估计受试者的体脂百分比，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，在分析摄影图像数据时可以执行得更准确，不需要重复的再训练，并消除主观性。我们的第一个具体目标是开发一种计算机图像分析算法，从男性、女性和儿童的双种族样本的标准化摄影图像中准确估计脂肪和骨骼肌质量。我们的第二个具体目标是在男性、女性和儿童的大量不同样本中验证该算法。