Dataset Information

Development and simulated validation of a dish composition database for estimating food group and nutrient intakes in Japan.

ABSTRACT:

Objective

To develop a dish composition database (DCD) and assess its ability to estimate dietary intake.

Design

The DCD was developed based on 16 d dietary records (DR). We aggregated all reported dishes into 128 dish codes and calculated mean food group and nutrient contents for each code. These data were used to calculate dietary intake in a different population that completed a 4 d DR. The estimated values were compared with those estimated using the standard food composition database (FCD) of Japan.

Setting

Japan.

Participants

A total 252 adults aged 31-81 years for the 16 d DR (3941 d in total) and 392 adults aged 20-69 years for the 4 d DR (1568 d in total) participated.

Results

There were significant differences in median intakes between the DCD and the FCD for eighteen and twenty (of twenty-six) food groups and for twenty-nine and twenty-two (of forty-three) nutrients (including energy) in men and women, respectively. For food group intakes, Spearman correlation coefficients between the DCD and FCD ranged from 0·19 (animal fats) to 0·90 (fruits and alcoholic beverages) in men (median: 0·61) and from 0·25 (oils) to 0·89 (noodles) in women (median: 0·58). For nutrient intakes, the corresponding values ranged from 0·25 (retinol) to 0·90 (alcohol) in men (median: 0·60) and from 0·15 (retinol) to 0·74 (alcohol) in women (median: 0·53).

Conclusions

Whereas it is difficult to accurately estimate absolute dietary intake values using the present DCD, it has acceptable ability to rank the intakes of many food groups and nutrients.

SUBMITTER: Shinozaki N

PROVIDER: S-EPMC10260419 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Development and simulated validation of a dish composition database for estimating food group and nutrient intakes in Japan.

Shinozaki Nana N Murakami Kentaro K Masayasu Shizuko S Sasaki Satoshi S

Public health nutrition 20190516 13

<h4>Objective</h4>To develop a dish composition database (DCD) and assess its ability to estimate dietary intake.<h4>Design</h4>The DCD was developed based on 16 d dietary records (DR). We aggregated all reported dishes into 128 dish codes and calculated mean food group and nutrient contents for each code. These data were used to calculate dietary intake in a different population that completed a 4 d DR. The estimated values were compared with those estimated using the standard food composition ...[more]

PMID: 31092299

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Project description:BackgroundAccurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are often prone to recall error and social desirability bias, limiting their reliability. With the advancement of artificial intelligence (AI), there is potential to overcome these limitations through automated, objective, and scalable dietary assessment techniques. However, the effectiveness and challenges of AI applications in this domain remain inadequately explored.ObjectiveThis study aimed to conduct a scoping review to synthesize existing literature on the efficacy, accuracy, and challenges of using AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement.MethodsThis review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive literature search was conducted in 4 databases-PubMed, Web of Science, Cochrane Library, and EBSCO-covering publications from the databases' inception to June 30, 2023. Studies were included if they used modern AI approaches to assess food and nutrient intakes in human subjects.ResultsThe 25 included studies, published between 2010 and 2023, involved sample sizes ranging from 10 to 38,415 participants. These studies used a variety of input data types, including food images (n=10), sound and jaw motion data from wearable devices (n=9), and text data (n=4), with 2 studies combining multiple input types. AI models applied included deep learning (eg, convolutional neural networks), machine learning (eg, support vector machines), and hybrid approaches. Applications were categorized into dietary intake assessment, food detection, nutrient estimation, and food intake prediction. Food detection accuracies ranged from 74% to 99.85%, and nutrient estimation errors varied between 10% and 15%. For instance, the RGB-D (Red, Green, Blue-Depth) fusion network achieved a mean absolute error of 15% in calorie estimation, and a sound-based classification model reached up to 94% accuracy in detecting food intake based on jaw motion and chewing patterns. In addition, AI-based systems provided real-time monitoring capabilities, improving the precision of dietary assessments and demonstrating the potential to reduce recall bias typically associated with traditional self-report methods.ConclusionsWhile AI demonstrated significant advantages in improving accuracy, reducing labor, and enabling real-time monitoring, challenges remain in adapting to diverse food types, ensuring algorithmic fairness, and addressing data privacy concerns. The findings suggest that AI has transformative potential for dietary assessment at both individual and population levels, supporting precision nutrition and chronic disease management. Future research should focus on enhancing the robustness of AI models across diverse dietary contexts and integrating biological sensors for a holistic dietary assessment approach.

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Dataset Information

Development and simulated validation of a dish composition database for estimating food group and nutrient intakes in Japan.

Objective

Design

Setting

Participants

Results

Conclusions

Publications

Development and simulated validation of a dish composition database for estimating food group and nutrient intakes in Japan.

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