HHSN261201400052C; TOPIC 308 AUTOMATED COLLECTION, STORAGE, ANALYSIS, AND REPORTING SYSTEMS FOR DIETARY IMAGES;TITLE: DDMP: A SYSTEM FOR AUTOMATED COLLECTION, STORAGE, ANALYSIS, AND REPORTING OF ANNOT

该项目的长期目标是开发膳食数据监测平台(DDMP)：一个集成的、可扩展的系统 用于临床和研究应用，收集和管理个人食物摄入量的数据。DDMP为 旨在减轻收集多天食物摄入量记录的负担，提高份量的准确性 测量，并简化患者/参与者培训、日程安排和数据处理流程。这一阶段 II努力将在一系列验证研究期间对DDMP进行Beta测试和最后确定。在Beta测试期间，界面、软件、 在第一阶段开发的报告工具将被重新评估用于临床和研究应用。链接到 NSDR、FNDDS、MPED和Gladson营养数据库将用于估计能量和营养摄入量 与重建的食物体积相关联。DDMP将利用高吞吐量、低延迟的数据收集 框架和直观的报告系统。该数据库将利用云计算和存储进行远程访问， 为临床和健康相关领域使用的不同数据收集平台提供亟需的改进 学习。这将在临床环境中实现更有效的营养干预，并使几种类型的 使用纸质食品记录不可行的研究设计(例如，病例队列研究)。

This project’s long-term goal is to develop the Dietary Data Monitoring Platform (DDMP): an integrated, scalable system for both clinical and research applications that collects and manages data on an individual’s food intake. DDMP is designed to reduce the burden of collecting multilpe-day records of food intake, improve the accuracy of serving size measurement, and streamline the processes of patient/participant training, scheduling and data processing. The Phase II effort will beta test and finalize DDMP during a series of validation studies. During beta testing, the interface, software, and reporting tools developed in Phase I will be reevaluated for clinical and research applications. Linkages to the NSDR, FNDDS, MPED, and the Gladson Nutritional Database will be used to estimate the energy and nutrient intake associated with the reconstructed food volume. DDMP will utilize a high throughput, low latency data collection framework and intuitive reporting system. The database will utilize cloud computing and storage for remote access, providing much needed improvement over disparate data collection platforms used clinically and in health-related studies. This will enable more efficient nutrition intervention in clinical settings and make possible several types of research designs (e.g., case-cohort studies) that are unfeasible using paper food records.