Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.Sensors (Basel). 2020;20. [PMID: 32664432 DOI: 10.3390/s20143870]

Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes-An Integrative Literature Review

Background/Objectives: Diabetes is a common public health disease that affects patients mentally, physically, and economically. It requires lifestyle changes such as blood sugar control and regular contact with healthcare services. Artificial intelligence has developed rapidly in many different areas in recent years, including healthcare and nursing. The aim of this study is to explore how artificial intelligence can be used as a tool for patients with diabetes mellitus. Methods: An integrative literature review design was chosen according to Whittemore and Knafl (2005). Electronic searches in databases were conducted across Pub-Med, CINAHL Complete (EBSCO), and ACM Digital Library until September 2024. A total set of quantitative and qualitative articles (n = 15) was selected and reviewed using a Mixed Method Appraisal Tool. Results: Artificial intelligence is an effective tool for patients with diabetes mellitus, and various models are used. Three themes emerged: artificial intelligence as a tool for blood sugar monitoring for patients with diabetes mellitus, artificial intelligence as a decision support for diabetic wounds and complications, and patients' requests for artificial intelligence capabilities in relation to tools. Artificial intelligence can create better conditions for patient self-care. Conclusions: Artificial intelligence is a valuable tool for patients with diabetes mellitus and enables the district nurse to focus more on person-centered care. The technology facilitates the patient's blood sugar monitoring. However, more research is needed to ensure the safety of AI technology, the protection of patient privacy, and clarification of laws and regulations within diabetes care.

An evaluation of YouTube videos on glucose sensor devices and type 1 diabetes mellitus: User perceptions, device features, and content reliability

AIM

The aim of this study is to evaluate the performance, comprehensiveness, reliability, and quality of English-language YouTube videos related to new glucose sensor devices.

METHODS

In November 2024, a search was conducted on a computer using the keywords "glucose sensor devices," "continuous glucose monitor," "glucose sensor devices and Type 1 Diabetes Mellitus," and "glucose sensor devices and child." Based on the inclusion and exclusion criteria, 30 videos that met the research objectives were analyzed. Relevant URLs were recorded. For each video, the following information was collected.

RESULTS

Of the 30 videos analyzed, 40 % (n = 12) were presented or managed by healthcare professionals. The average values for performance features of the videos were: 172,257.166 ± 233,861.720 views, 170.866 ± 222.974 comments, and 1,643.133 ± 2,252.247 likes. Four videos did not receive any likes. 40 % of the videos contained good and useful information for viewers, while 60 % were of high quality.

CONCLUSION

This study demonstrates that detailed and reliable content in YouTube videos about glucose monitoring devices enhances quality. It is recommended that video content be regularly evaluated, and future research should be conducted using alternative measurement tools in different languages.

A Narrative Review of the Interplay Between Carbohydrate Intake and Diabetes Medications: Unexplored Connections and Clinical Implications

This narrative review examines the dynamic interplay between carbohydrate intake and diabetes medications, highlighting their combined molecular and clinical effects on glycemic control. Carbohydrates, a primary energy source, significantly influence postprandial glucose regulation and necessitate careful coordination with pharmacological therapies, including insulin, metformin, glucagon-like peptide (GLP-1) receptor agonists, and sodium-glucose cotransporter-2 (SGLT2) inhibitors. Low-glycemic-index (GI) foods enhance insulin sensitivity, stabilize glycemic variability, and optimize medication efficacy, while high-GI foods exacerbate glycemic excursions and insulin resistance. Continuous glucose monitoring (CGM) offers real-time insights to tailor dietary and pharmacological interventions, improving glycemic outcomes and reducing complications. Despite advancements, gaps persist in understanding nutrient-drug interactions, particularly with emerging antidiabetic agents. This review underscores the need for integrating carbohydrate-focused dietary strategies with pharmacotherapy to enhance diabetes management. Future research should prioritize clinical trials leveraging CGM technology to explore how glycemic index, glycemic load, and carbohydrate quality interact with newer diabetes medications. Such studies can refine evidence-based recommendations, support individualized care plans, and improve long-term outcomes. Addressing systemic barriers, such as limited access to dietitians and CGM technology in underserved regions, is critical for equitable care. Expanding the roles of community health workers and training healthcare providers in basic nutrition counseling can bridge gaps, promoting sustainable and inclusive diabetes management strategies. These efforts are essential for advancing personalized, effective, and equitable care for individuals with diabetes.

AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges

With the aging of China's population and lifestyle changes, the number of patients with type 2 diabetes (T2D) has surged, posing a significant challenge to the public health system. This study explores the application and effectiveness of artificial intelligence (AI) technology in T2D management from a Chinese perspective. AI demonstrates substantial potential in personalized treatment planning, real-time monitoring and early warning, telemedicine, and health management. It not only enhances the precision and convenience of treatment but also aids in preventing and managing complications. Despite challenges in data privacy, technology popularization, standardization, and regulation, AI technology's continuous maturation and expanded application suggest its increasingly pivotal role in T2D management. In the future, through interdepartmental collaboration, policy support, and cultural adaptation, AI is poised to bring revolutionary changes to diabetes management in China and globally.

Application of artificial intelligence in the health management of chronic disease: bibliometric analysis

Background

With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration of artificial intelligence (AI) into chronic disease management has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented and lacks systematic, comprehensive analysis.

Objective

This study conducts a bibliometric analysis of AI applications in chronic disease health management, aiming to identify research trends, highlight key areas, and provide valuable insights into the current state of the field. Hoping our findings will serve as a useful reference for guiding further research and fostering the effective application of AI in healthcare.

Methods

The Web of Science Core Collection database was utilized as the source. All relevant publications from inception to August 2024 were retrieved. The external characteristics of the publications were summarized using HistCite. Keyword co-occurrences among countries, authors, and institutions were analyzed with Vosviewer, while CiteSpace was employed to assess keyword frequencies and trends.

Results

A total of 341 publications were retrieved, originating from 775 institutions across 55 countries, and published in 175 journals by 2,128 authors. A notable surge in publications occurred between 2013 and 2024, accounting for 95.31% (325/341) of the total output. The United States and the Journal of Medical Internet Research were the leading contributors in this field. Our analysis of the 341 publications revealed four primary research clusters: diagnosis, care, telemedicine, and technology. Recent trends indicate that mobile health technologies and machine learning have emerged as key focal points in the application of artificial intelligence in the field of chronic disease management.

Conclusion

Despite significant advancements in the application of AI in chronic disease management, several critical challenges persist. These include improving research quality, fostering greater international and inter-institutional collaboration, standardizing data-sharing practices, and addressing ethical and legal concerns. Future research should prioritize strengthening global partnerships to facilitate cross-disciplinary and cross-regional knowledge exchange, optimizing AI technologies for more precise and effective chronic disease management, and ensuring their seamless integration into clinical practice.

Wearable Aptasensors

This Perspective explores the revolutionary advances in wearable aptasensor (WA) technology, which combines wearable devices and aptamer-based detection systems for personalized, real-time health monitoring. The devices leverage the specificity and sensitivity of aptamers to target specific molecules, offering broad applications from continuous glucose tracking to early diagnosis of diseases. The integration of data analytics and artificial intelligence (AI) allows early risk prediction and guides preventive health measures. While challenges in miniaturization, power efficiency, and data security persist, these devices hold significant potential to democratize healthcare and reshape patient-doctor interactions.

Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes

Prediabetes, as an intermediary stage between normal glucose homeostasis and overt diabetes, affects an estimated 720 million individuals worldwide, highlighting the urgent need for proactive intervention strategies. Continuous glucose monitoring (CGM) emerges as a transformative tool, offering unprecedented insights into glycemic dynamics and facilitating tailored therapeutic interventions. This perspective scores the clinical significance of even slightly elevated fasting blood glucose levels and the critical role of early intervention. CGM technology provides real-time, continuous data on glucose concentrations, surpassing the constraints of conventional monitoring methods. Both retrospectively analyzed and real-time CGM systems offer valuable tools for glycemic management, each with unique strengths. The integration of CGM into routine care can detect early indicators of type 2 diabetes, inform the development of personalized intervention strategies, and foster patient engagement and empowerment. Despite challenges such as cost and the need for effective utilization through training and education, CGM's potential to revolutionize prediabetes management is evident. Future research should focus on refining CGM algorithms, exploring personalized intervention strategies, and leveraging wearable technology and artificial intelligence advancements to optimize glycemic control and patient well-being.

Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery

Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.

Macroergonomic components of the patient work system shaping dyadic care management during adolescence: a case study of type 1 diabetes

The role of the social, physical, and organisational environments in shaping how patients and their caregivers perform work remains largely unexplored in human factors/ergonomics literature. This study recruited 19 dyads consisting of a parent and their child with type 1 diabetes to be interviewed individually and analysed using a macroergonomic framework. Our findings aligned with the macroergonomic factors as presented in previous models, while highlighting the need to expand upon certain components to gain a more comprehensive representation of the patient work system as relevant to dyadic management. Examples of design efforts that should follow from these findings include expanding existing data sharing options to include information from the external environment and capitalising on the capabilities of artificial intelligence as a decision support system. Future research should focus on longitudinally assessing patient work systems throughout transition periods in addition to more explicitly exploring the roles of social network members.

Comparing the Effectiveness of Different Dietary Educational Approaches for Carbohydrate Counting on Glycemic Control in Adults with Type 1 Diabetes: Findings from the DIET-CARB Study, a Randomized Controlled Trial

BACKGROUND/OBJECTIVES

Carbohydrate counting is recommended to improve glycemic control in type 1 diabetes (T1D), but the most effective educational methods are unclear. Despite its benefits, many individuals struggle with mastering carbohydrate counting, leading to inconsistent use and suboptimal glycemic outcomes. This study aimed to compare the effectiveness of two group-based programs with individual dietary counseling (standard care) for glycemic control.

METHODS

The trial was a randomized, controlled, open-label, parallel-group design. Adults with T1D on multiple daily insulin injections (MDIs) and with glycated hemoglobin A1c (HbA1c) 53-97 mmol/mol were randomly assigned (1:1:1) to basic (BCC), advanced carbohydrate counting (ACC), or standard care. Primary outcomes were the changes in HbA1c or mean amplitude of glycemic excursions (MAGEs) in BCC and ACC versus standard care after six months. Equivalence testing was performed to compare BCC and ACC.

RESULTS

Between November 2018 and August 2021, 63 participants were randomly assigned to BCC (N = 20), ACC (N = 21), or standard care (N = 22). After 6 months, HbA1c changed by -2 mmol/mol (95% CI -5 to 0 [-0.2%, -0.5 to 0]) in BCC, -4 mmol/mol (-6 to -1 [-0.4%, -0.6 to -0.1]) in ACC, and -3 mmol/mol (-6 to 0 [-0.3%, -0.6 to 0]) in standard care. The estimated difference in HbA1c compared to standard care was 1 mmol/mol (-3 to 5 [0.1%, -0.3 to 0.5]); p = 0.663 for BCC and -1 mmol/mol (-4 to 3 [-0.1%, -0.4 to 0.3]); p = 0.779 for ACC. For MAGEs, changes were -0.3 mmol/L (-1.5 to 0.8) in BCC, -0.0 mmol/L (-1.2 to 1.1) in ACC, and -0.7 mmol/L (-1.8 to 0.4) in standard care, with differences of 0.4 mmol/L (-1.1 to 1.9); p = 0.590 for BCC and 0.7 mmol/L (-0.8 to 2.1); p = 0.360 for ACC versus standard care. An equivalence in effect between BCC and ACC was found for HbA1c, but not for MAGEs.

CONCLUSIONS

Group-based education in BCC and ACC did not demonstrate a clear advantage over individualized dietary counseling for overall glycemic control in adults with T1D. Healthcare providers should consider flexible, patient-centered strategies that allow individuals to choose the format that best suits their learning preferences when selecting the most suitable dietary educational approach.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis

Accurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance. Limited work has been made in this regard, which has resulted in different conclusions. This paper performs a comprehensive investigation of different data-driven time series forecasting approaches using different inputs. To do so, BGL prediction is comparatively investigated from two perspectives; the model's approach and the model's input. First, we compare the performance of BGL prediction using different data-driven time series forecasting approaches, including classical time series forecasting, traditional machine learning, and deep neural networks. Secondly, for each prediction approach, univariate input, using BGL data only, is compared to a multivariate input, using data on carbohydrate intake, injected bolus insulin, and physical activity in addition to BGL data. The investigation is performed on two publicly available Ohio datasets. Regression-based and clinical-based metrics along with statistical analyses are performed for evaluation and comparison purposes. The outcomes show that the traditional machine learning model is the fastest model to train and has the best BGL prediction performance especially when using multivariate input. Also, results show that simply adding extra variables does not necessarily improve BGL prediction performance significantly, and data fusion approaches may be required to effectively leverage other variables' information.

Mealtime prediction using wearable insulin pump data to support diabetes management

Many patients with diabetes struggle with post-meal high blood glucose due to missed or untimely meal-related insulin doses. To address this challenge, our research aims to: (1) study mealtime patterns in patients with type 1 diabetes using wearable insulin pump data, and (2) develop personalized models for predicting future mealtimes to support timely insulin dose administration. Using two independent datasets with over 45,000 meal logs from 82 patients with diabetes, we find that the majority of people ( ∼ 60%) have irregular and inconsistent mealtime patterns that change notably through the course of each day and across months in their own historical data. We also show the feasibility of predicting future mealtimes with personalized LSTM-based models that achieve an average F1 score of > 95% with less than 0.25 false positives per day. Our research lays the groundwork for developing a meal prediction system that can nudge patients with diabetes to administer bolus insulin doses before meal consumption to reduce the occurrence of post-meal high blood glucose.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App

BACKGROUND

Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.

METHODS

The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).

RESULTS

On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.

CONCLUSIONS

The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.

Continuous Glucose, Insulin and Lifestyle Data Augmentation in Artificial Pancreas Using Adaptive Generative and Discriminative Models

Artificial pancreas requires data from multiple sources for accurate insulin dose estimation. These include data from continuous glucose sensors, past insulin dosage information, meal quantity and time and physical activity data. The effectiveness of closed-loop diabetes management systems might be hampered by the absence of these data caused by device error or lack of compliance by patients. In this study, we demonstrate the effect of output sequence length-driven generative and discriminative model selection in high quality data generation and augmentation. This novel generative adversarial network (GAN) based architecture automatically selects the generator and discriminator architecture based on the desired output sequence length. The proposed model is able to generate glucose, physical activity, meal information data for individual patients. The discriminative scores for Ohio T1DM (2018) dataset were 0.17 ±0.03 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, Heart Rate, Steps) and for Ohio T1D (2020) dataset was 0.16 ±0.02 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, acceleration). A mixture of generated and real data was used to test predictive scores for glucose forecasting models. The best RMSE and MARD achieved for OhioT1DM patients were 17.19 ±3.22 and 7.14 ±1.76 for PH=30 min with CGM, CHO, Insulin, heartrate and steps as inputs. Similarly, the RMSE and MARD for real+synthetic data were 15.63 ±2.57 and 5.86 ±1.69 respectively. Compared to existing generative models, we demonstrate that sequence length based architecture selection leads to better synthetic data generation for multiple output sequences (CGM, CHO, Insulin) and forecasting accuracy.

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Periodontal disease and emerging point-of-care technologies for its diagnosis

Periodontal disease (PD), a chronic inflammatory disorder that damages the tooth and its supporting components, is a common global oral health problem. Understanding the intricacies of these disorders, from gingivitis to severe PD, is critical for efficient treatment, diagnosis, and prevention in dental care. Periodontal biosensors and biomarkers are critical in improving oral health diagnostic skills. Clinicians may accomplish early identification, tailored therapy, and efficient tracking of periodontal diseases by using these technologies, ushering in a new age of accurate oral healthcare. Traditional periodontitis diagnostic methods frequently rely on physical probing and visual examinations, necessitating the development of point-of-care (POC) devices. As periodontal disorders necessitate more precise and rapid diagnosis, incorporating novel innovations in biosensors and biomarkers becomes increasingly crucial. These innovations improve our capacity to diagnose, monitor, and adapt periodontal therapies, bringing in the next phase of customized and effective dental healthcare. The review discusses the characteristics and stages of PD, clinical treatment techniques, prominent biomarkers and infection-associated factors that may be employed to determine PD, biomedical sensing, and POC appliances that have been created so far to diagnose stages of PD and its progression profile, as well as predicting future developments in this field.

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review

Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.

Mixed methods study on the feasibility of implementing periodic continuous glucose monitoring among individuals with type 2 diabetes mellitus in a primary care setting

Background

Health care professionals (HCPs) play a central role in leveraging technologies to support individuals with diabetes. This mixed-method study was completed to determine the feasibility of implementing periodic continuous glucose monitoring (CGM) in a primary care setting.

Aim

This study aimed to evaluate and describe the experiences of using periodic CGM with data visualization tools in patients with type 2 diabetes to foster a person-centered approach in a primary care setting.

Methods

Fifty outpatients aged ≥18 years, diagnosed with type 2 diabetes, and with a disease duration of at least 2 years were included in this study. Data were collected from April 2021 to January 2022. Patients completed a single period of sensor measurements for 28 days and a diabetes questionnaire about feelings and experiences of health care. HbA1c was also measured. A focus group interview was conducted to evaluate and describe the HCPs experiences of using periodic CGM.

Results

Patients reported to HCPs that the CGM device was comfortable to wear and noted that LibreView was easy to use when scanning the sensor to obtain and visualize the glucose levels and trends. Data availability of CGM data was >70 %.Clinical observations revealed a mean reduction in HbA1c, mmol/mol from 60.06 [7.65 %] at baseline to 55.42 [7.20 %] after 4 weeks (p < 0.001). Two categories were identified: 1) Fostering dialogue on self-care and 2) Promoting understanding.

Conclusions

The HCPs and participants in this study had a positive experience or viewed the implementation of periodic CGM with data visualization tools as a positive experience and appeared to be feasible for implementation in a primary care setting.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.

Continuous Glucose Monitoring in Pregnancy

Diabetes mellitus in pregnancy is associated with adverse maternal and neonatal outcomes. Optimal glycemic control is associated with improved outcomes. Continuous glucose monitoring is a less invasive alternative to blood glucose measurements. Two types of continuous glucose monitoring are available in the market: real time and intermittently scanned. Continuous glucose monitoring is gaining popularity and is now recommended by some societies for glucose monitoring in pregnant women. In this review, we discuss the differences between the two types of continuous glucose monitoring, optimal treatment goals, and whether there is an improvement in maternal or neonatal outcomes.

A scoping review of artificial intelligence within pharmacy education

OBJECTIVES

This scoping review aimed to summarize the available literature on the use of artificial intelligence (AI) in pharmacy education and identify gaps where additional research is needed.

FINDINGS

Seven studies specifically addressing the use of AI in pharmacy education were identified. Of these 7 studies, 5 focused on AI use in the context of teaching and learning, 1 on the prediction of academic performance for admissions, and the final study focused on using AI text generation to elucidate the benefits and limitations of ChatGPT use in pharmacy education.

SUMMARY

There are currently a limited number of available publications that describe AI use in pharmacy education. Several challenges exist regarding the use of AI in pharmacy education, including the need for faculty expertise and time, limited generalizability of tools, limited outcomes data, and several legal and ethical concerns. As AI use increases and implementation becomes more standardized, opportunities will be created for the inclusion of AI in pharmacy education.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

American public opinion on artificial intelligence in healthcare

Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and "100 years from now." (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

BACKGROUND AND OBJECTIVE

Continuous glucose monitoring (CGM) sensors measure interstitial glucose concentration every 1-5 min for days or weeks. New CGM-based diabetes therapies are often tested in in silico clinical trials (ISCTs) using diabetes simulators. Accurate models of CGM sensor inaccuracies and failures could help improve the realism of ISCTs. However, the modeling of CGM failures has not yet been fully addressed in the literature. This work aims to develop a mathematical model of CGM gaps, i.e., occasional portions of missing data generated by temporary sensor errors (e.g., excessive noise or artifacts).

METHODS

Two datasets containing CGM traces collected in 167 adults and 205 children, respectively, using the Dexcom G6 sensor (Dexcom Inc., San Diego, CA) were used. Four Markov models, of increasing complexity, were designed to describe three main characteristics: number of gaps for each sensor, gap distribution in the monitoring days, and gap duration. Each model was identified on a portion of each dataset (training set). The remaining portion of each dataset (real test set) was used to evaluate model performance through a Monte Carlo simulation approach. Each model was used to generate 100 simulated test sets with the same size as the real test set. The distributions of gap characteristics on the simulated test sets were compared with those observed on the real test set, using the two-sample Kolmogorov-Smirnov test and the Jensen-Shannon divergence.

RESULTS

A six-state Markov model, having two states to describe normal sensor operation and four states to describe gap occurrence, achieved the best results. For this model, the Kolmogorov-Smirnov test found no significant differences between the distribution of simulated and real gap characteristics. Moreover, this model obtained significantly lower Jensen-Shannon divergence values than the other models.

CONCLUSIONS

A Markov model describing CGM gaps was developed and validated on two real datasets. The model describes well the number of gaps for each sensor, the gap distribution over monitoring days, and the gap durations. Such a model can be integrated into existing diabetes simulators to realistically simulate CGM gaps in ISCTs and thus enable the development of more effective and robust diabetes management strategies.

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.

Current Technologies for Managing Type 1 Diabetes Mellitus and Their Impact on Quality of Life-A Narrative Review

Type 1 diabetes mellitus is a chronic autoimmune disease that affects millions of people and generates high healthcare costs due to frequent complications when inappropriately managed. Our paper aimed to review the latest technologies used in T1DM management for better glycemic control and their impact on daily life for people with diabetes. Continuous glucose monitoring systems provide a better understanding of daily glycemic variations for children and adults and can be easily used. These systems diminish diabetes distress and improve diabetes control by decreasing hypoglycemia. Continuous subcutaneous insulin infusions have proven their benefits in selected patients. There is a tendency to use more complex systems, such as hybrid closed-loop systems that can modulate insulin infusion based on glycemic readings and artificial intelligence-based algorithms. It can help people manage the burdens associated with T1DM management, such as fear of hypoglycemia, exercising, and long-term complications. The future is promising and aims to develop more complex ways of automated control of glycemic levels to diminish the distress of individuals living with diabetes.

A Scoping Review of Artificial Intelligence Research in Rhinology

BACKGROUND

A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving.

OBJECTIVE

This scoping review aims to provide a brief overview of all current literature on AI in the field of rhinology. Further, it aims to highlight gaps in the literature for future rhinology researchers.

METHODS

OVID MEDLINE (1946-2022) and EMBASE (1974-2022) were searched from January 1, 2017 until May 14, 2022 to identify all relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was used to guide the review.

RESULTS

A total of 2420 results were identified of which 62 met the eligibility criteria. A further 17 articles were included through bibliography searching, for a total of 79 articles on AI in rhinology. Each year resulted in an increase in the number of publications, from 3 articles published in 2017 to 31 articles published in 2021. Articles were produced by authors from 22 countries with a relative majority coming from the USA (19%), China (19%), and South Korea (13%). Articles were placed into 1 of 5 categories: phenotyping/endotyping (n = 12), radiological diagnostics (n = 42), prognostication (n = 10), non-radiological diagnostics (n = 7), surgical assessment/planning (n = 8). Diagnostic or prognostic utility of the AI algorithms were rated as excellent (n = 29), very good (n = 25), good (n = 7), sufficient (n = 1), bad (n = 2), or was not reported/not applicable (n = 15).

CONCLUSIONS

AI is experiencing an increasingly significant role in rhinology research. Articles are showing high rates of diagnostic accuracy and are being published at an almost exponential rate around the world. Utilizing AI in radiological diagnosis was the most published topic of research, however, AI in rhinology is still in its infancy and there are several topics yet to be thoroughly explored.

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.

Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

Automated Insulin Delivery: Benefits, Challenges, and Recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialized for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organizations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organization has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonization of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organizations for confirmation.

Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

Insulin pumps in children - a systematic review

BACKGROUND

Insulin pump therapy is a real breakthrough in managing diabetes Mellitus, particularly in children. It can deliver a tiny amount of insulin and decreases the need for frequent needle injections. It also helps to maintain adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues. Children are suitable candidates for pump therapy as they need a more freestyle and proper metabolic control to ensure adequate growth and development. Therefore, children and their caregivers should have proper education and training and understand the proper use of insulin pumps to achieve successful pump therapy. The pump therapy continuously improves to enhance its performance and increase its simulation of the human pancreas. Nonetheless, there is yet a long way to reach the desired goal.

AIM

To review discusses the history of pump development, its indications, types, proper use, special conditions that may enface the children and their families while using the pump, its general care, and its advantages and disadvantages.

METHODS

We conducted comprehensive literature searches of electronic databases until June 30, 2022, related to pump therapy in children and published in the English language.

RESULTS

We included 118 articles concerned with insulin pumps, 61 were reviews, systemic reviews, and meta-analyses, 47 were primary research studies with strong design, and ten were guidelines.

CONCLUSION

The insulin pump provides fewer needles and can provide very tiny insulin doses, a convenient and more flexible way to modify the needed insulin physiologically, like the human pancreas, and can offer adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues.

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes mellitus. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.

Bridging Gaps in Diabetic Nephropathy Care: A Narrative Review Guided by the Lived Experiences of Patient Partners

Purpose of review

Diabetes affects almost a 10th of the Canadian population, and diabetic nephropathy is one of its main complications. It remains a leading cause of kidney failure despite the availability of effective treatments.

Sources of information

The sources of information are iterative discussions between health care professionals and patient partners and literature collected through the search of multiple databases.

Methods

Major pitfalls related to optimal diabetic nephropathy care were identified through discussions between patient partners and clinician researchers. We identified underlying factors that were common between pitfalls. We then conducted a narrative review of strategies to overcome them, with a focus on Canadian initiatives.

Key findings

We identified 5 pitfalls along the diabetic nephropathy trajectory, including a delay in diabetes diagnosis, suboptimal glycemic control, delay in the detection of kidney involvement, suboptimal kidney protection, and deficient management of advanced chronic kidney disease. Several innovative care models and approaches have been proposed to address these pitfalls; however, they are not consistently applied. To improve diabetic nephropathy care in Canada, we recommend focusing initiatives on improving awareness of diabetic nephropathy, improving access to timely evidence-based care, fostering inclusive patient-centered care environment, and generating new evidence that supports complex disease management. It is imperative that patients and their families are included at the center of these initiatives.

Limitations

This review was limited to research published in peer-reviewed journals. We did not perform a systematic review of the literature; we included articles that were relevant to the major pitfalls identified by our patient partners. Study quality was also not formally assessed. The combination of these factors limits the scope of our conclusions.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Integration of people with diabetes’ vision in the development process to improve self-management via diabetes Apps: A qualitative study (Preprint)

BACKGROUND

Diabetes is a major global epidemic and serious public health problem. Diabetes self-management is a 24/7 challenge for people with type 1 diabetes that influences their quality of life (QoL). Certain apps can support the self-management of people with diabetes; however, current apps do not meet the needs of people with diabetes appropriately, and their safety is not ensured. Moreover, there are a multitude of hardware and software problems associated with diabetes apps and regulations. Clear guidelines are required to regulate medical care via apps. In Germany, apps must undergo 2 examination processes to be listed in the Digitale Gesundheitsanwendungen directory. However, neither examination process considers whether the medical use of the apps is sufficient for users' self-management.

OBJECTIVE

This study aims to contribute to the technology development process of diabetes apps by exploring individual perspectives on desired features and content of diabetes apps among people with diabetes. The vision assessment conducted is a first step toward creating a shared vision among all relevant stakeholders. To ensure adequate research and development processes for diabetes apps in the future, guiding visions from all relevant stakeholders are required.

METHODS

In a qualitative study, 24 semistructured interviews with patients with type 1 diabetes were conducted, among whom 10 (42%) were currently using an app. To clarify the perceptions of people with diabetes regarding the functions and content of diabetes apps, a vision assessment was conducted.

RESULTS

People with diabetes have concrete ideas of features and content in apps to improve their QoL and allow them to live as comfortably as possible, such as informative predictions through artificial intelligence, improvements in signal loss and value delay through smartwatches, improved communication and information-sharing capabilities, reliable information sources, and user-friendly and discreet messaging options through smartwatches. In addition, according to people with diabetes, future apps should show improved sensors and app connectivity to avoid incorrect values being displayed. They also wish for an explicit indication that displayed values are delayed. In addition, personalized information was found to be lacking in apps.

CONCLUSIONS

People with type 1 diabetes want future apps to improve their self-management and QoL and reduce stigma. Desired key features include personalized artificial intelligence predictions of blood glucose levels, improved communication and information sharing through chat and forum options, comprehensive information resources, and smartwatch alerts. A vision assessment is the first step in creating a shared vision among stakeholders to responsibly guide the development of diabetes apps. Relevant stakeholders include patient organizations, health care professionals, insurers, policy makers, device manufacturers, app developers, researchers, medical ethicists, and data security experts. After the research and development process, new apps must be launched while considering regulations regarding data security, liability, and reimbursement.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

Accessibility and Openness to Diabetes Management Support via Mobile Phones: A Survey of People with Type 1 Diabetes Using Advanced Diabetes Technologies (Preprint)

Background

Little is known about the feasibility of mobile health (mHealth) support among people with type 1 diabetes (T1D) using advanced diabetes technologies including continuous glucose monitoring (CGM) systems and hybrid closed-loop insulin pumps (HCLs).

Objective

This study aims to evaluate patient access and openness to receiving mHealth diabetes support in people with T1D using CGM systems or HCLs.

Methods

We conducted a cross-sectional survey among patients with T1D using CGM systems or HCLs managed in an academic medical center. Participants reported information regarding their mobile device use; cellular call, SMS text message, or internet connectivity; and openness to various channels of mHealth communication (smartphone apps, SMS text messages, and interactive voice response [IVR] calls). Participants’ demographic characteristics and CGM data were collected from medical records. The analyses focused on differences in openness to mHealth and mHealth communication channels across groups defined by demographic variables and measures of glycemic control.

Results

Among all participants (N=310; female: n=198, 63.9%; mean age 45, SD 16 years), 98.1% (n=304) reported active cellphone use and 80% (n=248) were receptive to receiving mHealth support to improve glucose control. Among participants receptive to mHealth support, 98% (243/248) were willing to share CGM glucose data for mHealth diabetes self-care assistance. Most (176/248, 71%) were open to receiving messages via apps, 56% (139/248) were open to SMS text messages, and 12.1% (30/248) were open to IVR calls. Older participants were more likely to prefer SMS text messages (P=.009) and IVR calls (P=.03) than younger participants.

Conclusions

Most people with T1D who use advanced diabetes technologies have access to cell phones and are receptive to receiving mHealth support to improve diabetes control.

Sensing Glucose Concentration Using Symmetric Metasurfaces under Oblique Incident Terahertz Waves

In this article, a planar metamaterial sensor designed at terahertz (THz) frequencies is utilized to sense glucose concentration levels that cover hypoglycemia, normal, and hyperglycemia conditions that vary from 54 to 342 mg/dL. The sensor was developed using a symmetric complementary split rectangular resonator at an oblique incidence angle. The resonance frequency shift was used as a measure of the changes in the glucose level of the samples. The increase in the glucose concentration level exhibited clear and noticeable redshifts in the resonance frequency. For instance, a 67.5 GHz redshift has been observed for a concentration level of 54 mg/dL and increased up to 122 GHz for the 342 mg/dL concentration level. Moreover, a high sensitivity level of 75,700 nm/RIU was observed for this design. In the future, the proposed THz sensors may have potential applications in diagnosing hypocalcemia and hyperglycemia cases.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Pros and cons of continuous glucose monitoring in the intensive care unit

Diabetes mellitus affects people worldwide, and management of its acute complications or treatment-related adverse events is particularly important in critically ill patients. Previous reports have confirmed that hyperglycemia can increase the risk of mortality in patients cared in the intensive care unit (ICU). In addition, severe and multiple hypoglycemia increases the risk of mortality when using insulin or intensive antidiabetic therapy. The innovation of continuous glucose monitoring (CGM) may help to alert medical caregivers with regard to the development of hyperglycemia and hypoglycemia, which may decrease the potential complications in patients in the ICU. The major limitation of CGM is the measurement of interstitial glucose levels rather than real-time blood glucose levels; thus, there will be a delay in the treatment of hyperglycemia and hypoglycemia in patients. Recently, the European Union approved a state-of-art artificial intelligence directed loop system coordinated by CGM and a continuous insulin pump for diabetes control, which may provide a practical way to prevent acute adverse glycemic events related to antidiabetic therapy in critically ill patients. In this mini-review paper, we describe the application of CGM to patients in the ICU and summarize the pros and cons of CGM.