Evidence map›Paper›PMID 42056109›Full record

ArticleScientific data2026

Chinese Food Images for Full-cycle Nutrition Analysis Towards Diabetes Management.

Yuanxin Jin, Ming Li, Qinpei Zhao, Chenwei Wu, Shuang Liu, Lu Yao, Jessie Chen, Jiangfeng Li, Weixiong Rao, Juliang Xu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. 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. 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

11 authors.

Yuanxin Jin *School of Electronic Information, Shanghai DianJi University, Shanghai, China.
Ming LiSchool of Electronic Information, Shanghai DianJi University, Shanghai, China. mingli@sdju.edu.cn.ORCID http://orcid.org/0000-0002-0303-1668
Qinpei ZhaoSchool of Computer Science and Technology, Tongji University, Shanghai, China. qinpeizhao@tongji.edu.cn.ORCID http://orcid.org/0000-0002-1765-1171
Chenwei Wu *School of Computer Science and Technology, Tongji University, Shanghai, China.
Shuang Liu *Department of Endocrinology & Metabolism, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Lu YaoDepartment of Endocrinology & Metabolism, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Jessie ChenUniversity of Eastern Finland, Joensuu, Finland.
Jiangfeng LiSchool of Computer Science and Technology, Tongji University, Shanghai, China. lijf@tongji.edu.cn.
Weixiong RaoSchool of Computer Science and Technology, Tongji University, Shanghai, China.
Juliang XuShanghai Fengxian District Central Hospital, Shanghai, China. juliang62025@163.com.
Congrong WangDepartment of Endocrinology & Metabolism, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China. crwang@tongji.edu.cn.

Funding

Shanghai Science and Technology Development Foundation (Shanghai Science and Technology Development Fund) 22410713200
6 · The paper itself

Abstract

Public food image datasets have primarily focused on recognition or segmentation, with limited resources for comprehensive nutrition analysis, particularly for Chinese cuisine which exhibits high nutritional variability due to diverse cooking methods and regional influences. To address this gap, we introduce a multimodal food image dataset designed to support full-cycle nutritional analysis, including segmentation, category recognition, and nutrient content estimation, tailored specifically to diabetic diets. This dataset is aligned with clinical data from Chinese diabetes cohorts to facilitate accurate dietary management and glucose prediction. It provides a valuable resource for advancing computational methods in meal-level nutrition assessment for diabetic populations.

Indexed as

Diabetes MellitusFoodNutrition AssessmentChinaHumans

Identifiers

PMID42056109
PMCPMC13333986

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.