Chotwanvirat Phawinpon, Hnoohom Narit, Rojroongwasinkul Nipa, Kriengsinyos Wantanee
Doctor of Philosophy Program in Nutrition, Faculty of Medicine, Ramathibodi Hospital, The Institute of Nutrition, Mahidol University, Salaya, Thailand.
Department of Computer Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand.
Front Nutr. 2021 Oct 18;8:732449. doi: 10.3389/fnut.2021.732449. eCollection 2021.
Carbohydrate counting is essential for well-controlled blood glucose in people with type 1 diabetes, but to perform it precisely is challenging, especially for Thai foods. Consequently, we developed a deep learning-based system for automatic carbohydrate counting using Thai food images taken from smartphones. The newly constructed Thai food image dataset contained 256,178 ingredient objects with measured weight for 175 food categories among 75,232 images. These were used to train object detector and weight estimator algorithms. After training, the system had a Top-1 accuracy of 80.9% and a root mean square error (RMSE) for carbohydrate estimation of <10 g in the test dataset. Another set of 20 images, which contained 48 food items in total, was used to compare the accuracy of carbohydrate estimations between measured weight, system estimation, and eight experienced registered dietitians (RDs). System estimation error was 4%, while estimation errors from nearest, lowest, and highest carbohydrate among RDs were 0.7, 25.5, and 7.6%, respectively. The RMSE for carbohydrate estimations of the system and the lowest RD were 9.4 and 10.2, respectively. The system could perform with an estimation error of <10 g for 13/20 images, which placed it third behind only two of the best performing RDs: RD1 (15/20 images) and RD5 (14/20 images). Hence, the system was satisfactory in terms of accurately estimating carbohydrate content, with results being comparable with those of experienced dietitians.
对于1型糖尿病患者而言，碳水化合物计数对于良好控制血糖至关重要，但精确进行碳水化合物计数具有挑战性，尤其是对于泰国食物。因此，我们开发了一种基于深度学习的系统，利用从智能手机拍摄的泰国食物图像自动进行碳水化合物计数。新构建的泰国食物图像数据集包含256,178个成分对象，在75,232张图像中有175种食物类别的测量重量。这些数据用于训练目标检测器和重量估计器算法。训练后，该系统在测试数据集中的Top-1准确率为80.9%，碳水化合物估计的均方根误差（RMSE）<10克。另一组共包含48种食物的20张图像用于比较测量重量、系统估计和八位经验丰富的注册营养师（RD）之间碳水化合物估计的准确性。系统估计误差为4%，而RD中最接近、最低和最高碳水化合物估计误差分别为0.7%、25.5%和7.6%。系统和最低RD的碳水化合物估计RMSE分别为9.4和10.2。该系统在13/20张图像上的估计误差<10克，仅次于表现最佳的两位RD：RD1（15/20张图像）和RD5（14/20张图像），位列第三。因此，该系统在准确估计碳水化合物含量方面令人满意，结果与经验丰富的营养师相当。
