JMIR Publications

ABSTRACT

Background:

Accurately capturing an individual's food intake often requires dietitians to ask about food, frequency, and portion, relying on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-Assisted Dietary Assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a background to fully understand.

Objective:

This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible way for those unfamiliar with AI terminology. Additionally, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community.

Methods:

This scoping review utilized the PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021.

Results:

A total of 522 articles were initially identified. Based on a rigorous selection process, 84 articles were ultimately included in this review. These selected articles demonstrate that the sequential processes of segmentation, food identification, portion estimation, and nutrient calculations are consistently present in early existing systems. Notably, significant advancements have been made in the fields of food identification and portion estimation, aiming to replicate human-like performance. By analyzing the selected articles, we were able to construct a timeline that highlights the gradual enhancements achieved in these two key algorithms: food identification and food portion size estimation.

Conclusions:

This review highlights the progress made in IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.