Evidence map›Paper›PMID 41550432›Full record

ArticleJournal of food engineering2024

Image-Based Volume Estimation for Food in a Bowl.

Wenyan Jia, Boyang Li, Qi Xu, Guangzong Chen, Zhi-Hong Mao, Megan A McCrory, Tom Baranowski, Lora E Burke, Benny Lo, Alex K Anderson and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of food engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Wenyan JiaDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Boyang LiDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Qi XuHuazhong University of Science and Technology, Wuhan, China.
Guangzong ChenDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Zhi-Hong MaoDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Megan A McCroryDepartment of Health Sciences, College of Health & Rehabilitation Sciences, Boston University, Boston, MA, USA.
Tom BaranowskiUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Lora E BurkeSchool of Nursing, University of Pittsburgh, PA, USA.
Benny LoHamlyn Centre, Imperial College London, London, UK.
Alex K AndersonDepartment of Nutritional Sciences, College of Family and Consumer Sciences, University of Georgia, Athens, GA, USA.
Gary FrostSection for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Edward SazonovDepartment of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA.
Mingui SunDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.

Funding

A Human-Mimetic AI System for Automatic, Passive and Objective Dietary AssessmentR01DK127310 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SUN, MINGUI · 2021 to 2024
$2.5M
Automatic Recognition of Diet, Physical Activity and Sedentary Behavior Using a Smart Wearable DeviceR56DK113819 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SUN, MINGUI · 2018 to 2018
$490k
NIDDK NIH HHS R01 DK127310NIDDK NIH HHS R56 DK113819
6 · The paper itself

Abstract

Image-assisted dietary assessment has become popular in dietary monitoring studies in recent years. However, food volume estimation is still a challenging problem due to the lack of 3D information in a 2D image and the occlusion of the food by itself or container (e.g., bowl, cup). This study aims to investigate the relationship between the observable surface of food in a bowl and a normalized index (i.e., bowl fullness) to represent its volume. A mathematical model is established for describing different shapes of bowls, and a convenient experimental method is proposed to determine the bowl shape. An image feature called Food Area Ratio (FAR) is used to estimate the volume of food in a bowl based on the relationship between bowl fullness and the FAR calculated from the image. Both simulations and experiments with real food/liquid demonstrate the feasibility and accuracy of the proposed approach.

Indexed as

Bowl shapeFood volume estimationImage-assisted dietary assessment

Identifiers

PMID41550432
PMCPMC12805954

What OpenQuestion holds

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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.