TY -非盟的Sudo,恭子盟——紫衣,Kazuhiko盟——Kinebuchi Tetsuya盟——木村,Shigeko盟——Waki击倒PY - 2020 DA - 2020/10/26 TI -基于机器学习从图像分析筛选健康的食物:系统开发和试点研究乔- Res JMIR形式SP - e18507六世- 4 - 10 KW -餐图像KW -健康KW -深层神经网络KW -营养千瓦医学信息学KW -饮食KW -神经网络AB -背景:最近的研究导致了许多信息技术支持的卫生保健控制系统的发展，包括从食物图像估计营养的系统。捕捉饮食和运动数据的系统对糖尿病患者和仅仅在节食的人都很有用。持续监测是有效饮食控制的关键，需要使用简单并能激励用户注意饮食的系统。不幸的是，目前大多数系统都很复杂，或者没有激励作用。这样的系统需要一些手动输入，比如选择图标或图像，或者输入用户食物的类别。反馈给用户的营养信息并不是特别有用，因为通常只提供膳食中所含的估计的详细营养价值。目的:在本文中，我们介绍了膳食健康作为一个更有用和有意义的通用标准，并提出了一种新的算法，可以从膳食图像中估计健康，而不需要人工输入。方法:我们提出了一个系统，使用提取特征的深度神经网络和使用由人类饮食专家准备的数据集学习饮食健康程度之间关系的排名网络来评估膳食健康程度。首先，我们研究了注册营养师是否可以仅通过使用一个小数据集(100顿饭)查看膳食图像来判断膳食的健康程度。 We then generated ranking data based on comparisons of sets of meal images (850 meals) by a registered dietitian’s viewing meal images and trained a ranking network. Finally, we estimated each meal’s healthiness score to detect unhealthy meals. Results: The ranking estimated by the proposed network and the ranking of healthiness based on the dietitian’s judgment were correlated (correlation coefficient 0.72). In addition, extracting network features through pretraining with a publicly available large meal dataset enabled overcoming the limited availability of specific healthiness data. Conclusions: We have presented an image-based system that can rank meals in terms of the overall healthiness of the dishes constituting the meal. The ranking obtained by the proposed method showed a good correlation to nutritional value–based ranking by a dietitian. We then proposed a network that allows conditions that are important for judging the meal image, extracting features that eliminate background information and are independent of location. Under these conditions, the experimental results showed that our network achieves higher accuracy of healthiness ranking estimation than the conventional image ranking method. The results of this experiment in detecting unhealthy meals suggest that our system can be used to assist health care workers in establishing meal plans for patients with diabetes who need advice in choosing healthy meals. SN - 2561-326X UR - http://formative.www.mybigtv.com/2020/10/e18507/ UR - https://doi.org/10.2196/18507 UR - http://www.ncbi.nlm.nih.gov/pubmed/33104010 DO - 10.2196/18507 ID - info:doi/10.2196/18507 ER -