IEEE J Biomed Health Inform. 2020 Jul;24(7):1926-1939. doi: 10.1109/JBHI.2020.2987943. Epub 2020 Apr 30.
A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.
一种名为 24 小时膳食回顾的日常膳食评估方法在营养流行病学研究中被广泛应用，以获取参与者所吃食物的详细信息，帮助了解他们的饮食行为。然而，在这种自我报告技术中，报告的食物类型和份量高度依赖于用户的主观判断，这可能导致饮食分析结果出现偏差和不准确。因此，最近提出了各种基于视觉的饮食评估方法。虽然这些方法在解决营养流行病学研究中的问题方面显示出了前景，但正如本研究详细说明的那样，仍然存在一些挑战和即将到来的机遇。本研究概述了图像基饮食评估领域中使用的计算算法、数学模型和方法。它还全面比较了食品识别和体积/重量估计的最新方法在处理速度、模型准确性、效率和限制方面的优缺点。接下来将讨论深度学习方法及其在饮食评估中的功效。经过全面探索，我们发现，结合不同方法的综合饮食评估系统可能是解决准确饮食摄入评估挑战的潜在解决方案。
