Evidence map›Paper›PMID 41286499›Full record

ArticleScientific reports2025

AI powered dietary proportion assessment for improving accuracy and practicality of the balanced meal plate model.

Worasit Choochaiwattana, Patinya Jaruariyanon, Assaree Jitpranee, Rawirin Deecharoen, Thanaporn Kaewpradup, Charoonsri Chusak, Sirichai Adisakwattana

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In one paragraph

Article in Scientific reports, 2025. 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
–field-weighted citation impact
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. [Transformation of Epidemiology in the Age of Artificial Intelligence].Revista medica del Instituto Mexicano del Seguro Social · 2026
    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

7 authors.

Worasit ChoochaiwattanaDepartment of Statistics, Chulalongkorn Business School, Chulalongkorn University, Bangkok, 10330, Thailand.
Patinya JaruariyanonDepartment of Statistics, Chulalongkorn Business School, Chulalongkorn University, Bangkok, 10330, Thailand.
Assaree JitpraneeDepartment of Nutrition and Dietetics, Faculty of Allied Health Sciences, Center of Excellence in Phytochemical and Functional Food for Clinical Nutrition , Chulalongkorn University, Bangkok, 10330, Thailand.
Rawirin DeecharoenDepartment of Nutrition and Dietetics, Faculty of Allied Health Sciences, Center of Excellence in Phytochemical and Functional Food for Clinical Nutrition , Chulalongkorn University, Bangkok, 10330, Thailand.
Thanaporn KaewpradupDepartment of Nutrition and Dietetics, Faculty of Allied Health Sciences, Center of Excellence in Phytochemical and Functional Food for Clinical Nutrition , Chulalongkorn University, Bangkok, 10330, Thailand.
Charoonsri ChusakDepartment of Nutrition and Dietetics, Faculty of Allied Health Sciences, Center of Excellence in Phytochemical and Functional Food for Clinical Nutrition , Chulalongkorn University, Bangkok, 10330, Thailand. Charoonsri.c@chula.ac.th.
Sirichai AdisakwattanaDepartment of Nutrition and Dietetics, Faculty of Allied Health Sciences, Center of Excellence in Phytochemical and Functional Food for Clinical Nutrition , Chulalongkorn University, Bangkok, 10330, Thailand.

Funding

National Research Council of Thailand NRCT: N42A680622the Ratchadaphiseksomphot Fund, Chulalongkorn University Grant No. DNS_67_045_3700_001
6 · The paper itself

Abstract

The 2:1:1 dietary proportion model, promoting balanced eating, faces challenges in practical application due to difficulty in visually estimating portion sizes. To address this, we developed an artificial intelligence (AI)-based application to assist in the accurate assessment of 2:1:1 dietary proportion. This study demonstrated the accuracy of the AI application compared to estimates made by nutrition and dietetics students (ND) and registered dietitians (RD), while also assessing user satisfaction and attitudes. The AI application was trained using images of three popular Thai dishes: Hainanese Chicken Rice, Shrimp Paste Fried Rice, and Egg Noodle, each prepared in three portion variations. The AI system demonstrated significantly lower mean absolute error (MAE) than both ND and RD groups in estimating proportions for Hainanese Chicken Rice and Shrimp Paste Fried Rice (p < 0.05), indicating superior accuracy. User satisfaction surveys revealed that 61% of participants rated their overall experience with the application as moderate, with suggestions for improving accuracy. Over half agreed that the AI tool shows potential as a practical resource for nutrition education and dietary assessments. The AI holds promise as a tool for promoting adherence to the 2:1:1 model and facilitating healthy lifestyle changes.

Indexed as

Artificial IntelligenceDietMealsAdultFemaleHumansMaleNutrition AssessmentThailandYoung Adult2:1:1 proportionArtificial intelligenceDietary assessmentPlate modelWeb application

Identifiers

PMID41286499
PMCPMC12749368

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.