Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Nutrients, Journal Year: 2024, Volume and Issue: 16(7), P. 1073 - 1073

Published: April 6, 2024

In industry 4.0, where the automation and digitalization of entities processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. this context, nutrition, critical aspect public health, no exception to fields influenced by integration AI technology. This study aims comprehensively investigate current landscape providing deep understanding potential AI, machine learning (ML), (DL) nutrition sciences highlighting eventual challenges futuristic directions. A hybrid approach from systematic literature review (SLR) guidelines preferred reporting items for reviews meta-analyses (PRISMA) was adopted systematically analyze scientific search major databases on sciences. rigorous selection conducted using most appropriate eligibility criteria, followed methodological quality assessment ensuring robustness included studies. identifies several applications spanning smart personalized dietary assessment, food recognition tracking, predictive modeling disease prevention, diagnosis monitoring. The selected studies demonstrated versatility techniques handling complex relationships within nutritional datasets. provides comprehensive overview state opportunities. With rapid advancement its into holds significant promise enhance individual outcomes optimize recommendations. Researchers, policymakers, healthcare professionals can utilize research design future projects support evidence-based decision-making guidance.

Language: Английский

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Artificial Intelligence for Dietary Management

Advances in medical diagnosis, treatment, and care (AMDTC) book series, Journal Year: 2024, Volume and Issue: unknown, P. 276 - 307

Published: Aug. 9, 2024

Artificial intelligence (AI) is increasingly becoming a pivotal tool in the field of dietary management, offering innovative solutions for personalized nutrition and health optimization. This chapter examines application AI technologies managing habits improving nutritional outcomes. It covers various techniques, including machine learning, natural language processing, computer vision, used to analyze interpret vast amounts data. The authors discuss how can provide tailored recommendations, monitor eating behaviors, predict deficiencies. Real-world examples case studies are presented demonstrate efficacy potential AI-driven management systems. By integrating into this highlights transformative intelligent systems enhancing individual preventing diet-related diseases.

Language: Английский

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An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition

Nutrients, Journal Year: 2025, Volume and Issue: 17(2), P. 362 - 362

Published: Jan. 20, 2025

Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which vital distinguishing visually similar foods or images where the context of whole dish is crucial, thus necessitating transformer architecture. Objectives: This research explores capabilities CNNs transformers build robust classification model that can handle both short- features accurately classify food enhance recognition better analysis. Methods: Our approach, combines Vision Transformers (ViTs), begins RestNet50 backbone model. responsible local extraction from input image. The resulting map then passed ViT encoder block, handles further using multi-head attention fully connected layers pre-trained weights. Results: experiments on five datasets have confirmed superior performance compared current state-of-the-art methods, our combined dataset leveraging complementary showed enhanced generalizability addressing diversity. We explainable techniques like grad-CAM LIME understand how models made decisions, thereby enhancing user’s trust proposed system. been integrated into mobile application nutrition analysis, offering an intelligent diet-tracking Conclusion: paves way practical personalized healthcare, showcasing extensive potential AI sciences various dietary

Language: Английский

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AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2147 - 2147

Published: March 28, 2025

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), Internet Things (IoT), and data-driven approaches, address various challenges in food sector. It encompasses a wide range technologies that improve efficiency, safety, sustainability systems, from production consumption. represents transformative approach addressing sector by integrating AI, IoT, methodologies. Unlike traditional which primarily focus on leverages AI for intelligent decision making IoT real-time monitoring, enabling significant advancements areas supply chain optimization, personalized nutrition. This review highlights applications, including computer vision recognition quality assessment, Natural Language Processing recipe analysis, predictive modeling dietary recommendations. Simultaneously, enhances transparency efficiency through data collection, device connectivity. The convergence these relies diverse sources, images, nutritional databases, user-generated logs, are critical traceability tailored solutions. Despite its potential, faces challenges, heterogeneity, privacy concerns, scalability issues, regulatory constraints. To these, this paper explores solutions like federated learning secure on-device processing blockchain transparent traceability. Emerging trends, edge analytics sustainable practices powered AI-IoT integration, also discussed. offers actionable insights advance innovative ethical technological frameworks.

Language: Английский

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Utilizing applications Nutrihas Pro for calculated fluid and electrolyte requirements for patient.

Nutrición clínica y dietética hospitalaria/Nutrición clínica, dietética hospitalaria, Journal Year: 2025, Volume and Issue: 45(1)

Published: Feb. 3, 2025

Background: Hospital malnutrition is a critical issue, particularly in regions like Makassar, Indonesia, where rates surpass national averages. Malnourished patients often experience electrolyte imbalances and prolonged hospital stays, leading to increased healthcare costs. Despite the importance of accurate nutritional therapy, manual calculations are time-consuming prone human error, necessitating more efficient solution. Objective: This study aims assess effectiveness Nutrihas-Pro application, developed improve accuracy time efficiency therapy planning compared methods. Methods: An experimental repeated measures design was employed, involving 30 clinical nutrition residents at RSUP Dr. Wahidin Sudirohusodo. Participants manually calculated fluid/electrolyte needs for 60 process using Nutrihas-Pro. Calculation times were paired-samples t-tests chi- square tests. Results: The application significantly reduced calculation (p = 0.000) methods, without compromising fluid requirement > 0.05). Patients displayed high prevalence imbalance (68.3%), hyponatremia (35%). Conclusion: improves while maintaining accuracy, making it promising tool management. Further research needed address its limitations, including reliance on internet connectivity comparisons with other calculator applications.

Language: Английский

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A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images

Applied Biosciences, Journal Year: 2025, Volume and Issue: 4(1), P. 9 - 9

Published: Feb. 5, 2025

This research reviews deep learning methodologies for detecting leukemia, a critical cancer diagnosis and treatment aspect. Using systematic mapping study (SMS) literature review (SLR), thirty articles published between 2019 2023 were analyzed to explore the advancements in techniques leukemia using blood smear images. The analysis reveals that state-of-the-art models, such as Convolutional Neural Networks (CNNs), transfer learning, Vision Transformers (ViTs), ensemble methods, hybrid achieved excellent classification accuracies. Preprocessing including normalization, edge enhancement, data augmentation, significantly improved model performance. Despite these advancements, challenges dataset limitations, lack of interpretability, ethical concerns regarding privacy bias remain barriers widespread adoption. highlights need diverse, well-annotated datasets development explainable AI models enhance clinical trust usability. Additionally, addressing regulatory integration is essential safe deployment technologies healthcare. aims guide researchers overcoming advancing applications improve diagnostics patient outcomes.

Language: Английский

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Empirical analysis of smart eating habits

Cleaner and Responsible Consumption, Journal Year: 2025, Volume and Issue: unknown, P. 100271 - 100271

Published: March 1, 2025

Language: Английский

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Personalized Nutrition in Healthcare Using IoT for Tailored Dietary Solutions

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 401 - 424

Published: Feb. 7, 2025

Personalized nutrition is precision health that forms personalized diets based on the genetic, environmental, and lifestyle characteristics of an individual. It further improves with integration Internet Things in collecting, analyzing, feedback mechanisms real time, enhancing adaptation nutritional interventions: glucose levels, body composition, diet are monitored wearables, smart appliances, connected systems. The data, thus processed, then channeled through AI algorithms to derive personal recommendations tailored goals, medical conditions, preferences Healthcare providers can use IoT gain more effective, sustainable result better patient outcomes for chronic diseases, weight management, well-being. chapter analyses technological advancements, challenges, potential IoT-enabled transforming modern healthcare fostering a customized approach toward diet-based interventions.

Language: Английский

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Mobile Health and Artificial Intelligence as Nutritional Support for the Population: A Review

Published: June 5, 2024

Mobile applications, websites and social media networks are nowadays widely used communication tools. With the emergence of communication-related technologies in our lives and, consequently, rise mobile health-related generically encompassed under term digital health, have become popular among population. Smartphones artificial intelligence very useful tools for interventions. Because they accessible cost-effective. They also able to serve a larger number communities than traditional Nutrition is not field that has remained on sidelines, numerous applications technological emerged intended help support diets or process recovering from disease. However, many these limitations important consider. For this reason, aim review was analyze most currently use, discuss their advantages disadvantages, propose hypotheses improvement.

Language: Английский

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From Bench to Bedside: Translating Cellular Rejuvenation Therapies into Clinical Applications

Cells, Journal Year: 2024, Volume and Issue: 13(24), P. 2052 - 2052

Published: Dec. 12, 2024

Cellular rejuvenation therapies represent a transformative frontier in addressing age-related decline and extending human health span. By targeting fundamental hallmarks of aging—such as genomic instability, epigenetic alterations, mitochondrial dysfunction, cellular senescence—these aim to restore youthful functionality cells tissues, offering new hope for treating degenerative diseases. Recent advancements have showcased range strategies, including reprogramming, senolytic interventions, restoration, stem cell-based approaches, gene-editing technologies like CRISPR. Each modality has demonstrated substantial potential preclinical models is now being cautiously explored early-stage clinical trials. However, translating these from the laboratory practice presents unique challenges: safety concerns, delivery precision, complex regulatory requirements, ethical considerations, high costs impede widespread adoption. This review examines current landscape rejuvenation, highlighting key advancements, risks, strategies needed overcome hurdles.

Language: Английский

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