About this Research Topic
As of 2019, approximately 463 million individuals aged 20 to 79 globally were diagnosed with diabetes mellitus (DM), and projections indicate that this figure is expected to rise to 700 million by 2045. DM stands among the top 10 factors contributing to global mortality. There is an urgent need to improve the diagnosis, monitoring, and treatment of this growing patient population.
Nutrition management or diet is considered as one critical dimension in creating an accurate treatment plan for diabetics. Good nutrition plays a central role in the management of diabetes at all ages. In addition, and because diabetics could have other complications, foods could have contradictions with different medications. Personalized and automatic management of these challenges is expected to improve the quality of life for DM patients.
Digital health tools and applications, especially those enhanced with artificial intelligence (AI) and machine learning (ML), are instigating a transformative change in the field of medicine, offering data-centric, customized approaches to DM management. AI-based applications can automatically oversee nutritional objectives aiming to maintain blood glucose levels close to normal. These techniques are expected to provide a more personalized, efficient, and effective approach to the management of DM.
This Research Topic focuses on the nutrients and diet interventions regarding the prevention and management of DM as well as the prevention of its complications. The issue encompasses meal planning strategies, customized treatment plans, nutritional prescriptions, and recommendations for carbohydrate consistency. It also addresses weight management, energy requirements, macronutrient and micronutrient needs, and nutritional considerations during exercise. Different techniques can be used to build smart, customized, interactive, and dynamic treatment plans for diabetics based on their specific conditions. These techniques include computer vision techniques to calculate the number of carbs in the meal, IoMT to provide real-time monitoring, and ontology to provide semantics for food interactions, to name a few.
This Research Topic provides the researchers with the opportunity to publish their high-quality and original experiences, recent results, advancements, surveys, and future directions in different dimensions of the role of nutrition in diabetes management and control. Manuscripts should address the methodological developments concerning dietary management in individuals with DM, especially type 1 diabetes. Diverse themes of research and experiment studies are welcome including the application of AI, computer vision, trustworthy AI, DM treatment planning, IoMT, sensor networks, ontology, knowledge-based models, mobile apps, medical systematic reviews, and meta-analyses for diet in diabetics. Research topics of interest include (but are not limited to) the following:
• AI-based personalized dietary recommendation system.
• Multimodal, visual, and causal explainability.
• Remote patient monitoring.
• Methodologies for dietary education for DM.
• Clinical decision support system for diet control.
• Computer vision models for estimating carbs in meals.
• Semantic food-drug interaction detection and management.
• Integrated electronic health record for diet management in DM patients.
• Role of mHealth in diabetic patients’ behavioral changes and treatment adherence.
• Methods for nutrition management for diabetic patients suffering from other autoimmune disorders e.g., celiac diseases.
• Impact of social, technological, and cultural factors on nutritional management of diabetes.
• Nutritional management and quality of life improvement in diabetic patients.
This Research Topic welcomes manuscripts focused on methodology development in the nutritional management of diabetes. Manuscripts that report studies and clinical trials about the effect of a particular dietary intervention on diabetes outcomes are outside of the scope of this Research Topic.
Nutrition management or diet is considered as one critical dimension in creating an accurate treatment plan for diabetics. Good nutrition plays a central role in the management of diabetes at all ages. In addition, and because diabetics could have other complications, foods could have contradictions with different medications. Personalized and automatic management of these challenges is expected to improve the quality of life for DM patients.
Digital health tools and applications, especially those enhanced with artificial intelligence (AI) and machine learning (ML), are instigating a transformative change in the field of medicine, offering data-centric, customized approaches to DM management. AI-based applications can automatically oversee nutritional objectives aiming to maintain blood glucose levels close to normal. These techniques are expected to provide a more personalized, efficient, and effective approach to the management of DM.
This Research Topic focuses on the nutrients and diet interventions regarding the prevention and management of DM as well as the prevention of its complications. The issue encompasses meal planning strategies, customized treatment plans, nutritional prescriptions, and recommendations for carbohydrate consistency. It also addresses weight management, energy requirements, macronutrient and micronutrient needs, and nutritional considerations during exercise. Different techniques can be used to build smart, customized, interactive, and dynamic treatment plans for diabetics based on their specific conditions. These techniques include computer vision techniques to calculate the number of carbs in the meal, IoMT to provide real-time monitoring, and ontology to provide semantics for food interactions, to name a few.
This Research Topic provides the researchers with the opportunity to publish their high-quality and original experiences, recent results, advancements, surveys, and future directions in different dimensions of the role of nutrition in diabetes management and control. Manuscripts should address the methodological developments concerning dietary management in individuals with DM, especially type 1 diabetes. Diverse themes of research and experiment studies are welcome including the application of AI, computer vision, trustworthy AI, DM treatment planning, IoMT, sensor networks, ontology, knowledge-based models, mobile apps, medical systematic reviews, and meta-analyses for diet in diabetics. Research topics of interest include (but are not limited to) the following:
• AI-based personalized dietary recommendation system.
• Multimodal, visual, and causal explainability.
• Remote patient monitoring.
• Methodologies for dietary education for DM.
• Clinical decision support system for diet control.
• Computer vision models for estimating carbs in meals.
• Semantic food-drug interaction detection and management.
• Integrated electronic health record for diet management in DM patients.
• Role of mHealth in diabetic patients’ behavioral changes and treatment adherence.
• Methods for nutrition management for diabetic patients suffering from other autoimmune disorders e.g., celiac diseases.
• Impact of social, technological, and cultural factors on nutritional management of diabetes.
• Nutritional management and quality of life improvement in diabetic patients.
This Research Topic welcomes manuscripts focused on methodology development in the nutritional management of diabetes. Manuscripts that report studies and clinical trials about the effect of a particular dietary intervention on diabetes outcomes are outside of the scope of this Research Topic.
Keywords: Dietary Management, Diabetes Mellitus, Glycemic Control, Artificial Intelligence, Precision Medicine, Mobile Apps, Computer Vision Models, Trustworthy AI, Drug Interaction, Autoimmune Disorders
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