PDF(Pubmed)
Abstract：
Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be implemented for the nutrition assessment of restaurant food. In this paper, we crowdsource 15,908 food images of 470 restaurants in the Greater Hartford region on Tripadvisor and Google Place. These food images are loaded into a proprietary deep learning model (Calorie Mama) for nutrition assessment. We employ manual coding to validate the model accuracy based on the Food and Nutrient Database for Dietary Studies. The derived nutrition information is visualized at both the restaurant level and the census tract level. The deep learning model achieves 75.1% accuracy when compared with manual coding. It has more accurate labels for ethnic foods but cannot identify portion sizes, certain food items (e.g., specialty burgers and salads), and multiple food items in an image. The restaurant nutrition (RN) index is further proposed based on the derived nutrition information. By identifying the nutrition information of restaurant food through crowdsourced food images and a deep learning model, the study provides a pilot approach for large-scale nutrition assessment of the community food environment.
摘要：
深度学习模型可以识别图像中的食物，并获得它们的营养信息。包括卡路里,大量营养素（碳水化合物，脂肪，和蛋白质），和微量营养素（维生素和矿物质）。该技术尚未用于餐厅食品的营养评估。在本文中,我们在Tripadvisor和GooglePlace上众包了大哈特福德地区470家餐厅的15,908张食物图像。这些食物图像被加载到专有的深度学习模型（CalorieMama）中进行营养评估。我们使用手动编码来验证基于膳食研究的食物和营养数据库的模型准确性。所得到的营养信息在餐厅级别和普查区级别都被可视化。与手动编码相比，深度学习模型的准确率为75.1%。它为民族食品提供了更准确的标签，但无法识别份量，某些食品(例如，特色汉堡和沙拉)，和图像中的多个食物项目。基于派生的营养信息进一步提出了餐厅营养(RN)指数。通过众包食物图像和深度学习模型识别餐厅食物的营养信息,该研究为社区食物环境的大规模营养评估提供了试点方法。
