Evidence map›Paper›PMID 38973336›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.

Peihua Ma, Yixin Wu, Ning Yu, Xiaoxue Jia, Yiyang He, Yang Zhang, Michael Backes, Qin Wang, Cheng-I Wei

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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. Review
  2. Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    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

9 authors.

Peihua MaDepartment of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.ORCID 0000-0002-5041-0361
Yixin WuCISPA Helmholtz Center for Information Security, 66123, Saarbrucken, Germany.
Ning YuNetflix Eyeline Studios, Los Angeles, CA, 90028, USA.
Xiaoxue JiaDepartment of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.ORCID 0000-0002-4783-3777
Yiyang HeDepartment of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.
Yang ZhangCISPA Helmholtz Center for Information Security, 66123, Saarbrucken, Germany.
Michael BackesCISPA Helmholtz Center for Information Security, 66123, Saarbrucken, Germany.ORCID 0000-0002-7130-9211
Qin WangDepartment of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.
Cheng-I WeiDepartment of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.ORCID 0000-0001-9764-3181

Funding

Maryland Agricultural Experiment Station MD-NFSC-232696Pamela Pehrsson and Jaspreet Ahuja
6 · The paper itself

Abstract

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high-throughput solution for nutritional analysis.

Indexed as

High-Throughput Screening AssaysFood AnalysisHumansNutrition Assessmentfood analysishigh‐throughput screeningmachine learningprecision nutritionvision‐language model

Identifiers

PMID38973336
PMCPMC11425866

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.