Clinical Nutrition Research Centre, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore 117599, Singapore.
Department of Biochemistry, Yong Loo Lin School of Medicine, Singapore 117596, Singapore.
Nutrients. 2020 Apr 22;12(4):1167. doi: 10.3390/nu12041167.
Obesity is a global health problem with wide-reaching economic and social implications. Nutrition surveillance systems are essential to understanding and addressing poor dietary practices. However, diets are incredibly diverse across populations and an accurate diagnosis of individualized nutritional issues is challenging. Current tools used in dietary assessment are cumbersome for users, and are only able to provide approximations of dietary information. Given the need for technological innovation, this paper reviews various novel digital methods for food volume estimation and explores the potential for adopting such technology in the Southeast Asian context. We discuss the current approaches to dietary assessment, as well as the potential opportunities that digital health can offer to the field. Recent advances in optics, computer vision and deep learning show promise in advancing the field of quantitative dietary assessment. The ease of access to the internet and the availability of smartphones with integrated cameras have expanded the toolsets available, and there is potential for automated food volume estimation to be developed and integrated as part of a digital dietary assessment tool. Such a tool may enable public health institutions to be able to gather an effective nutritional insight and combat the rising rates of obesity in the region.
肥胖是一个全球性的健康问题,具有广泛的经济和社会影响。营养监测系统对于了解和解决不良饮食行为至关重要。然而,不同人群的饮食差异极大,准确诊断个体的营养问题具有挑战性。目前用于饮食评估的工具使用起来很繁琐,只能提供饮食信息的近似值。鉴于需要技术创新,本文综述了各种用于食物量估计的新颖数字方法,并探讨了在东南亚背景下采用这种技术的潜力。我们讨论了当前的饮食评估方法,以及数字健康可以为该领域带来的潜在机会。光学、计算机视觉和深度学习的最新进展有望推动定量饮食评估领域的发展。互联网的便捷访问以及具有集成摄像头的智能手机的普及,扩大了可用的工具集,并且有可能开发和整合自动食物量估计作为数字饮食评估工具的一部分。这样的工具可以使公共卫生机构能够有效地收集营养信息,应对该地区不断上升的肥胖率。