Chotwanvirat Phawinpon, Prachansuwan Aree, Sridonpai Pimnapanut, Kriengsinyos Wantanee
Human Nutrition Unit, Food and Nutrition Academic and Research Cluster, Institute of Nutrition, Mahidol University, Nakhon Pathom, Thailand.
Diabetes and Thyroid Center, Theptarin Hospital, Khlong Toei, Bangkok, Thailand.
Curr Dev Nutr. 2024 Apr 4;8(5):102154. doi: 10.1016/j.cdnut.2024.102154. eCollection 2024 May.
Dietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors.
To address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence-based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images.
INMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set.
This combination significantly enhances the system's ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices.
INMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.
饮食评估是营养研究的基本组成部分，在慢性病管理中起着关键作用。传统的饮食评估方法，尤其是在泰国菜肴的背景下，通常需要大量培训，并且可能导致估计误差。
为应对这些挑战，玛希隆大学营养研究所（INMU）的iFood是一种基于人工智能的创新泰国食物饮食评估系统，可根据食物图像估算菜肴的营养价值。
INMU iFood利用先进技术并集成了经过验证的自动化泰国食物分析系统。用户可以使用3种不同的输入方法：食物图像识别、手动输入和便捷的条形码扫描仪。这种多功能性简化了饮食摄入量的跟踪，同时在个体层面上最大限度地提高了数据质量。INMU iFood的核心改进可归因于两个关键因素，即使用Yolov7取代Yolov4-tiny以及在训练图像数据集中扩展非碳水化合物源食物。
这种组合显著增强了系统识别食物项目的能力，尤其是在食物图像密集的场景中，从而提高了准确性。验证结果表明，基于INMU iFood集成的V7系统比其基于V4的前身具有更优越的性能，在蛋白质和脂肪估计方面有显著改进。此外，INMU iFood通过为用户提供通过条形码扫描仪导入其他食品的选项来解决局限性，从而通过开放食品数据库（Open Food Facts）提供对大量营养信息的访问。这种集成确保用户能够有效地跟踪他们的饮食摄入量，通过开放食品数据库中添加或更新的3000多种食物，涵盖了广泛的饮食选择。
INMU iFood对于寻求在泰国菜肴背景下监测其饮食摄入量的研究人员、医疗保健专业人员和个人来说是一个有前途的工具，最终有助于促进更好的健康结果并推动营养相关研究。
