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2
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J Control Release. 2022 Mar;343:31-42. doi: 10.1016/j.jconrel.2022.01.001. Epub 2022 Jan 6.
3
Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.自动化饮食摄入跟踪需要深度细化的语义分割来纠正长期护理院中的视觉-容积不匹配。
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Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial.智能手机图像膳食评估应用与 3 天食物日记评估加拿大成年人膳食摄入量的有效性和可用性:随机对照试验。
JMIR Mhealth Uhealth. 2020 Sep 9;8(9):e16953. doi: 10.2196/16953.

基于多媒体数据的膳食评估移动应用程序。

Multimedia Data-Based Mobile Applications for Dietary Assessment.

机构信息

ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA.

出版信息

J Diabetes Sci Technol. 2023 Jul;17(4):1056-1065. doi: 10.1177/19322968221085026. Epub 2022 Mar 29.

DOI:10.1177/19322968221085026
PMID:35348398
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10348006/
Abstract

Diabetes mellitus (DM) and obesity are chronic medical conditions associated with significant morbidity and mortality. Accurate macronutrient and energy estimation could be beneficial in attempts to manage DM and obesity, leading to improved glycemic control and weight reduction, respectively. Existing dietary assessment methods are subject to major errors in measurement, are time consuming, are costly, and do not provide real-time feedback. The increasing adoption of smartphones and artificial intelligence, along with the advances in algorithms and hardware, allowed the development of technologies executed in smartphones that use food/beverage multimedia data as an input, and output information about the nutrient content in almost real time. Scope of this review was to explore the various image-based and video-based systems designed for dietary assessment. We identified 22 different systems and divided these into three categories on the basis of their setting for evaluation: laboratory (12), preclinical (7), and clinical (3). The major findings of the review are that there is still a number of open research questions and technical challenges to be addressed and end users-including health care professionals and patients-need to be involved in the design and development of such innovative solutions. Last, there is a clear need that these systems should be validated under unconstrained real-life conditions and that they should be compared with conventional methods for dietary assessment.

摘要

糖尿病(DM)和肥胖是与显著发病率和死亡率相关的慢性疾病。准确估计宏量营养素和能量可能有助于尝试管理 DM 和肥胖,从而分别改善血糖控制和减轻体重。现有的饮食评估方法在测量方面存在重大误差,既费时又昂贵,并且不能提供实时反馈。智能手机和人工智能的日益普及,以及算法和硬件的进步,使得可以开发在智能手机中执行的技术,这些技术使用食物/饮料多媒体数据作为输入,并几乎实时输出有关营养成分的信息。本综述的范围是探索用于饮食评估的各种基于图像和基于视频的系统。我们确定了 22 种不同的系统,并根据其评估设置将这些系统分为三类:实验室(12 种)、临床前(7 种)和临床(3 种)。审查的主要发现是,仍然存在许多开放的研究问题和技术挑战需要解决,并且包括医疗保健专业人员和患者在内的最终用户需要参与此类创新解决方案的设计和开发。最后,显然需要在不受限制的现实生活条件下验证这些系统,并将其与传统的饮食评估方法进行比较。