Review of the validity and feasibility of image-assisted methods for dietary assessment.
Review of the validity and feasibility of image-assisted methods for dietary assessment.
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DOI：
10.1038/s41366-020-00693-2
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Martin CK
中科院分区：
文献类型：
--
作者：
Höchsmann C;Martin CK
准确量化膳食摄入量对于了解膳食对健康的影响和评价膳食干预的效果至关重要。经常使用自我报告方法(例如，食物记录)，尽管这些方法在评估能量和营养摄入量方面明显不准确。通过食物图像评估食物摄入量的方法克服了传统自我报告的许多局限性。在自助餐厅的环境中，数码摄影已被证明是不引人注目和准确的，是评估食物供应、盘子浪费和食物摄入量的首选方法。在自由生活的条件下，通过用户的智能手机捕捉食物选择和餐盘垃圾的图像是有希望的，可以产生准确的能量摄入量估计，尽管准确性不能得到保证。这些方法促进了数据的(接近)实时传输，并消除了用户对分量大小估计的需要，因为食物图像是由训练有素的评分员分析的。类似于参与者必须如实记录所有消费食物的自我报告方法，仍然存在的一个限制是由于社会愿望或健忘而故意和/或无意地少报食物。依赖于通过可穿戴式摄像头进行被动图像捕获的方法前景看好，旨在减轻用户的负担；然而，目前只有有效性有限的飞行员数据可用，这些方法仍然引人注目和繁琐。为了减少与分析相关的工作人员的负担，并允许向用户提供实时反馈，最近的方法旨在使食品图像的分析自动化。然而，支持自动食物识别和分量大小估计的技术仍处于起步阶段，以可接受的精度进行全自动食物摄入量评估尚未成为现实。这篇综述进一步评估了当前图像辅助食物摄入量评估方法的好处和挑战，并得出结论：负担较轻的方法不太准确，目前的方法不足以在所有环境中使用。
Accurately quantifying dietary intake is essential to understanding the effect of diet on health and evaluating the efficacy of dietary interventions. Self-report methods (e.g., food records) are frequently utilized despite evident inaccuracy of these methods at assessing energy and nutrient intake. Methods that assess food intake via images of foods have overcome many of the limitations of traditional self-report. In cafeteria settings, digital photography has proven to be unobtrusive and accurate and is the method of choice for assessing food provision, plate waste, and food intake. In free-living conditions, image capture of food selection and plate waste via the user’s smartphone, is promising and can produce accurate energy intake estimates, though accuracy is not guaranteed. These methods foster (near) real-time transfer of data and eliminate the need for portion size estimation by the user since the food images are analyzed by trained raters. A limitation that remains, similar to self-report methods where participants must truthfully record all consumed foods, is intentional and/or unintentional under-reporting of foods due to social desirability or forgetfulness. Methods that rely on passive image capture via wearable cameras are promising and aim to reduce user burden; however, only pilot data with limited validity are currently available and these methods remain obtrusive and cumbersome. To reduce analysis-related staff burden and to allow real-time feedback to the user, recent approaches have aimed to automate the analysis of food images. The technology to support automatic food recognition and portion size estimation is, however, still in its infancy and fully-automated food intake assessment with acceptable precision not yet a reality. This review further evaluates the benefits and challenges of current image-assisted methods of food intake assessment and concludes that less burdensome methods are less accurate and that no current method is adequate in all settings.
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影响因子：
5.9
作者：
Aflague TF;Boushey CJ;Guerrero RT;Ahmad Z;Kerr DA;Delp EJ
通讯作者：
Delp EJ
影响因子：
3.2
作者：
Jia, Wenyan;Chen, Hsin-Chen;Yue, Yaofeng;Li, Zhaoxin;Fernstrom, John;Bai, Yicheng;Li, Chengliu;Sun, Mingui
通讯作者：
Sun, Mingui
影响因子：
3.7
作者：
Altazan AD;Gilmore LA;Burton JH;Ragusa SA;Apolzan JW;Martin CK;Redman LM
通讯作者：
Redman LM
影响因子：
9.3
作者：
Gibson, Rosalind S.;Charrondiere, U. Ruth;Bell, Winnie
通讯作者：
Bell, Winnie
影响因子：
5
作者：
Boushey, Carol Jo;Harray, Amelia J.;Delp, Edward J.
通讯作者：
Delp, Edward J.