ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland.
Division of Endocrinology, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA.
Sensors (Basel). 2020 Jul 31;20(15):4283. doi: 10.3390/s20154283.
Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOOD. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOOD requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food's volume. Each meal's calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOOD supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOOD performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOOD provides a simple and efficient solution to the end-user for dietary assessment.
准确估计营养信息可能会导致更健康的饮食和更好的临床结果。我们提出了一种基于人工智能（AI）的饮食评估系统，名为 goFOOD。该系统可以仅根据智能手机拍摄的食物图像来估计膳食的卡路里和宏量营养素含量。goFOOD 需要输入两张膳食图像或短视频。对于传统的单摄像头智能手机，图像必须从两个不同的视角拍摄；配备两个后置摄像头的智能手机只需按下快门按钮一次。深度神经网络用于处理两张图像，并执行食物检测、分割和识别，而 3D 重建算法则估算食物的体积。根据食物类别、体积和营养数据库计算每顿饭的卡路里和宏量营养素含量。goFOOD 支持 319 个精细的食物类别，并已在包含非标准化和快餐的两个多媒体数据库上进行了验证。实验结果表明，goFOOD 在非标准化膳食数据库上的表现优于经验丰富的营养师，而在快餐数据库上的表现与营养师相当。goFOOD 为最终用户提供了一种简单高效的饮食评估解决方案。
