Integration of USDA Food Classification System and Food Composition Database for Image-Based Dietary Assessment among Individuals Using Insulin.

Luotao Lin, Jiangpeng He, Fengqing Zhu, Edward J Delp, Heather A Eicher-Miller

Open access · goldAbstract read

In one paragraph

Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.3field-weighted citation impact, top 39% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.Nutrients · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Luotao LinDepartment of Nutrition Science, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA.

Jiangpeng HeSchool of Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN 47907, USA.

Fengqing ZhuSchool of Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-3863-3220

Edward J DelpSchool of Electrical and Computer Engineering, College of Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-2909-7323

Heather A Eicher-MillerDepartment of Nutrition Science, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-1261-4291

Purdue University West Lafayette · US

Funding

Eli Lilly and Company N/A

6 · The paper itself

Abstract

New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.

Indexed as

InsulinNutrition AssessmentDietFoodHumansUnited StatesUnited States Department of AgricultureInsulindatabase developmentfood categoryfood codesfood identificationfood image databasenutrient compositionnutrition assessment

Identifiers

PMID37513600

DOI10.3390/nu15143183

PMCPMC10385317

OpenAlexW4384824595

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.