Artificial Intelligence Technology for Food Nutrition
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Food nutrition is generally defined as the heat energy and nutrients obtained from food by the human body, such as protein, fat, carbohydrates and so on [...]
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AI-powered platforms, wearable devices, and continuous glucose monitors enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs.
“These tools enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [102]
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Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00503-2 · Cited by 914 · see citing works
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2021.100577 · Cited by 820 · see citing works
Recent advances and application of machine learning in food flavor prediction and regulation
Ji, H., Pu, D., Yan, W., et al. (2023) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2023.07.012 · Cited by 222 · see citing works
Strawberry sweetness and consumer preference are enhanced by specific volatile compounds
Fan, Z., Hasing, T. N., Johnson, T. S., Garner, D. M., Schwieterman, M. L., Barbey, C. R., Colquhoun, T. A., Sims, C. A., Resende, M. F. R., & Whitaker, V. M. (2021) · Horticulture Research · doi:10.1038/s41438-021-00502-5 · Cited by 203 · see citing works
Yang, L., Hsieh, C.-K., Yang, H., Pollak, J. P., Dell, N. L., Belongie, S., Cole, C. L., & Estrin, D. L. (2017) · ACM Transactions on Information Systems · doi:10.1145/3072614 · Cited by 182 · see citing works
Food Recommendation: Framework, Existing Solutions, and Challenges
Min, W., Jiang, S., & Jain, R. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2958761 · Cited by 164 · see citing works
Ellis, D. I., Broadhurst, D., & Goodacre, R. (2004) · Analytica Chimica Acta · doi:10.1016/j.aca.2004.03.060 · Cited by 122 · see citing works
Cao, E., Watt, M. J., Nowell, C. J., Quach, T., Simpson, J. S., Ferreira, V. D. M., Agarwal, S., Chu, H. X., Srivastava, A., Anderson, D., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00457-w · Cited by 104 · see citing works
A review on vision-based analysis for automatic dietary assessment
Wang, W., Min, W., Li, T., Dong, X., Li, H., & Jiang, S. (2022) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2022.02.017 · Cited by 91 · see citing works
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91 · see citing works
Market2Dish: Health-aware Food Recommendation
Wang, W., Duan, L., Jiang, H., Jing, P., Song, X., & Nie, L. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3418211 · Cited by 78 · see citing works
Hu, S., Li, H., Chen, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-07222-3 · Cited by 66 · see citing works
Healthy Nutrition: Preventing Nutrition-Related Diseases in Europe
James, W. P. T. (1990) · Annals of Internal Medicine · doi:10.7326/0003-4819-113-10-813_8 · Cited by 65 · see citing works
Joshi, S., Bisht, B., Kumar, V., Singh, N., Begum, J. P. S., Singh, N., & Kumar, S. (2023) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-023-00200-4 · Cited by 44 · see citing works
Yum-me: A Personalized Nutrient-based Meal Recommender SystemPreprint
Yang, L., Hsieh, C.-K., Yang, H., Dell, N. L., Belongie, S., Cole, C. L., & Estrin, D. L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07722 · Cited by 33 · see citing works
Ruede, R., Heußer, V., Frank, L., et al. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412839 · Cited by 28 · see citing works
Dietary patterns affect Parkinson's disease via the microbiota-gut-brain axis
Chu, C., Yu, L., Chen, W., Tian, F., & Zhai, Q. (2021) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2021.07.004 · Cited by 26 · see citing works
Statistical modeling of gut microbiota for personalized health status monitoring
Zhu, J., Xie, H., Yang, Z., Chen, J., Yin, J., Tian, P., Wang, H., Zhao, J., Zhang, H., Lu, W., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01614-x · Cited by 23 · see citing works
Self-citationWang, H., Song, W., Yuan, W., Zhou, Q., Sadiq, F. A., Zhao, J., Wu, W., & Lu, W. (2023) · Nutrients · doi:10.3390/nu15143101 · Cited by 17 · see citing works
Enhancement of nutritional value of fried fish using an artificial intelligence approach
Sadhu, T., Banerjee, I., Lahiri, S. K., et al. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13548-8 · Cited by 15 · see citing works
Chen, J., Zhu, J., Lu, W., Wang, H., Pan, M., Tian, P., Zhao, J., Zhang, H., & Chen, W. (2023) · Nutrients · doi:10.3390/nu15183925 · Cited by 14 · see citing works
Self-citationPartially Supervised Multi-Task Network for Single-View Dietary Assessment
Lu, Y. L., Stathopoulou, T., & Mougiakakou, S. G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412339 · Cited by 12 · see citing works
A Hybrid Network Based on GAN and CNN for Food Segmentation and Calorie Estimation
Jaswanthi, R., Amruthatulasi, E., Bhavyasree, C., et al. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760831 · Cited by 12 · see citing works
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Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024) · Nutrients · doi:10.3390/nu16071073 · Cited by 253 · see citing works
Theodorakis, N., Kreouzi, M., Pappas, A., & Nikolaou, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413438 · Cited by 43 · see citing works
- Authors
- Zhu, J., & Wang, G.
- Affiliations
- Jiangnan University (CN)
- Corresponding author
- Zhu, Jinlin
- Venue
- Nutrients
- Publisher
- MDPI AG
- Published
- 2023-10-27
- Volume
- 15
- Issue
- 21
- Pages
- 4562
- ISSN
- 2072-6643 (ISSN-L)
- PMC
- PMC10649930
- OpenAlex
- W4387971551
- Cited by
- 16
- References
- 23
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nutrients (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food and Agricultural Sciences
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, food nutrition, nutrient content, macronutrients, food composition
- MeSH terms
- Nutrients, Artificial Intelligence, Energy Intake, Carbohydrates, Food, Humans
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