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Artificial Intelligence in Nutrition Research: Current and Future Perspectives and Applications

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition Methodology & Assessment".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1162

Special Issue Editor

Prof. Dr. Dario Gregori

E-Mail Website
Guest Editor
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
Interests: public health nutrition; nutrition epidemiology; obesity epidemiology; machine learning and data mining in nutrition; biostatistics

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence (AI) are revolutionizing numerous fields, and nutrition research is no exception. This Special Issue of Nutrients explores the transformative potential of AI in addressing pressing challenges in nutritional science, promoting innovation in research methodologies, and paving the way for more personalized, precise, and effective nutritional interventions. The integration of AI-driven approaches offers novel insights into the complex interactions between diet, health, and disease, enabling researchers to analyze large-scale data sets, predict outcomes, and tailor recommendations with unprecedented accuracy.

This Special Issue will highlight cutting-edge research and applications of AI in nutrition, ranging from machine learning algorithms for dietary assessment to the predictive modeling of health outcomes and the development of smart tools for personalized nutrition. By bridging the gap between computational technology and nutritional science, these advancements promise to enhance our understanding of nutritional mechanisms and improve public health strategies.

As Guest Editor, I am delighted to invite researchers and practitioners to contribute original research articles and reviews that address current challenges, propose innovative AI methodologies, or showcase practical applications in the field of nutrition. Together, we aim at fostering a deeper understanding of the ways in which AI can revolutionize nutrition research and inspire future advancements in this rapidly evolving domain.

Prof. Dr. Dario Gregori
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in nutrition
  • machine learning in nutritional science
  • personalized nutrition
  • nutritional epidemiology
  • big data in nutrition research
  • dietary assessment tools
  • predictive modeling in nutrition
  • AI-driven public health strategies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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16 pages, 1417 KiB  
Article
Survival Modelling Using Machine Learning and Immune–Nutritional Profiles in Advanced Gastric Cancer on Home Parenteral Nutrition
by Konrad Matysiak, Aleksandra Hojdis and Magdalena Szewczuk
Nutrients 2025, 17(15), 2414; https://doi.org/10.3390/nu17152414 - 24 Jul 2025
Viewed by 111
Abstract
Background/Objectives: Patients with stage IV gastric cancer who develop chronic intestinal failure require home parenteral nutrition (HPN). This study aimed to evaluate the prognostic relevance of nutritional and immune–inflammatory biomarkers and to construct an individualised survival prediction model using machine learning techniques. Methods: [...] Read more.
Background/Objectives: Patients with stage IV gastric cancer who develop chronic intestinal failure require home parenteral nutrition (HPN). This study aimed to evaluate the prognostic relevance of nutritional and immune–inflammatory biomarkers and to construct an individualised survival prediction model using machine learning techniques. Methods: A secondary analysis was performed on a cohort of 410 patients with TNM stage IV gastric adenocarcinoma who initiated HPN between 2015 and 2023. Nutritional and inflammatory indices, including the Controlling Nutritional Status (CONUT) score and lymphocyte-to-monocyte ratio (LMR), were assessed. Independent prognostic factors were identified using Cox proportional hazards models. A Random Survival Forest (RSF) model was constructed to estimate survival probabilities and quantify variable importance. Results: Both the CONUT score and LMR were independently associated with overall survival. In multivariate analysis, higher CONUT scores were linked to increased mortality risk (HR = 1.656, 95% CI: 1.306–2.101, p < 0.001), whereas higher LMR values were protective (HR = 0.632, 95% CI: 0.514–0.777, p < 0.001). The RSF model demonstrated strong predictive accuracy (C-index: 0.985–0.986) and effectively stratified patients by survival risk. The CONUT score exerted the greatest prognostic influence, with the LMR providing additional discriminatory value. A gradual decline in survival probability was observed with an increasing CONUT score and a decreasing LMR. Conclusions: The application of machine learning to immune–nutritional data offers a robust tool for predicting survival in patients with advanced gastric cancer requiring HPN. This approach may enhance risk stratification, support individualised clinical decision-making regarding nutritional interventions, and inform treatment intensity adjustment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nutrition Research: Current and Future Perspectives and Applications)
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23 pages, 8902 KiB  
Article
2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project
by Rachele Bianco, Sergio Coluccia, Michela Marinoni, Alex Falcon, Federica Fiori, Giuseppe Serra, Monica Ferraroni, Valeria Edefonti and Maria Parpinel
Nutrients 2025, 17(13), 2196; https://doi.org/10.3390/nu17132196 - 30 Jun 2025
Viewed by 415
Abstract
Background/Objectives: Deep learning (DL) has shown strong potential in analyzing food images, but few studies have directly predicted mass, energy, and macronutrient content from images. In addition to the importance of high-quality data, differences in country-specific food composition databases (FCDBs) can hinder [...] Read more.
Background/Objectives: Deep learning (DL) has shown strong potential in analyzing food images, but few studies have directly predicted mass, energy, and macronutrient content from images. In addition to the importance of high-quality data, differences in country-specific food composition databases (FCDBs) can hinder model generalization. Methods: We assessed the performance of several standard DL models using four ground truth datasets derived from Nutrition5k—the largest image–nutrition dataset with ~5000 complex US cafeteria dishes. In light of developing an Italian dietary assessment tool, these datasets varied by FCDB alignment (Italian vs. US) and data curation (ingredient–mass correction and frame filtering on the test set). We evaluated combinations of four feature extractors [ResNet-50 (R50), ResNet-101 (R101), InceptionV3 (IncV3), and Vision Transformer-B-16 (ViT-B-16)] with two regression networks (2+1 and 2+2), using IncV3_2+2 as the benchmark. Descriptive statistics (percentages of agreement, unweighted Cohen’s kappa, and Bland–Altman plots) and standard regression metrics were used to compare predicted and ground truth nutritional composition. Dishes mispredicted by ≥7 algorithms were analyzed separately. Results: R50, R101, and ViT-B-16 consistently outperformed the benchmark across all datasets. Specifically, when replacing it with these top algorithms, reductions in median Mean Absolute Percentage Errors were 6.2% for mass, 6.4% for energy, 12.3% for fat, and 33.1% and 40.2% for protein and carbohydrates. Ingredient–mass correction substantially improved prediction metrics (6–42% when considering the top algorithms), while frame filtering had a more limited effect (<3%). Performance was consistently poor across most models for complex salads, chicken-based or eggs-based dishes, and Western-inspired breakfasts. Conclusions: The R101 and ViT-B-16 architectures will be prioritized in future analyses, where ingredient–mass correction and automated frame filtering methods will be considered. Full article
(This article belongs to the Special Issue Artificial Intelligence in Nutrition Research: Current and Future Perspectives and Applications)
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