Smart telemedicine support for continuous glucose monitoring: the embryo of a future global agent for diabetes care

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.

A probabilistic semantic analysis of eHealth scientific literature

INTRODUCTION

eHealth emerged as an interdisciplinary research area about 70 years ago. This study employs probabilistic techniques to semantically analyse scientific literature related to the field of eHealth in order to identify topics and trends and discuss their comparative evolution.

METHODS

Authors collected titles and abstracts of published literature on eHealth as indexed in PubMed. Basic statistical and bibliometric techniques were applied to overall describe the collected corpus; Latent Dirichlet Allocation was employed for unsupervised topics identification; topics trends analysis was performed, and correlation graphs were plotted were relevant.

RESULTS

A total of 30,425 records on eHealth were retrieved from PubMed (all records till 31 December 2017, search on 8 May 2018) and 23,988 of these were included to the study corpus. eHealth domain shows a growth higher than the growth of the entire PubMed corpus, with a mean increase of eHealth corpus proportion of about 7% per year for the last 20 years. Probabilistic topics modelling identified 100 meaningful topics, which were organised by the authors in nine different categories: general; service model; disease; medical specialty; behaviour and lifestyle; education; technology; evaluation; and regulatory issues.

DISCUSSION

Trends analysis shows a continuous shift in focus. Early emphasis on medical image transmission and system integration has been replaced by increased focus on standards, wearables and sensor devices, now giving way to mobile applications, social media and data analytics. Attention on disease is also shifting, from initial popularity of surgery, trauma and acute heart disease, to the emergence of chronic disease support, and the recent attention to cancer, infectious disease, mental disorders, paediatrics and perinatal care; most interestingly the current swift increase is in research related to lifestyle and behaviour change. The steady growth of all topics related to assessment and various systematic evaluation techniques indicates a maturing research field that moves towards real world application.

Gestational Diabetes Management Using Smart Mobile Telemedicine

Gestational diabetes (GDM) burden has been increasing progressively over the past years. Knowing that intrauterine exposure to maternal diabetes confers high risk for macrosomia as well as for future type 2 diabetes and obesity of the offspring, health care organizations try to provide effective control in spite of the limited resources. Artificial-intelligence-augmented telemedicine has been proposed as a helpful tool to facilitate an efficient widespread medical assistance to GDM. The aim of the study we present was to test the feasibility and acceptance of a mobile decision-support system for GDM, developed in the seventh framework program MobiGuide Project, which includes computer-interpretable clinical practice guidelines, access to data from the electronic health record as well as from glucose, blood pressure, and activity sensors. The results of this pilot study with 20 patients showed that the system is feasible. Compliance of patients with blood glucose monitoring was higher than that observed in a historical group of 247 patients, similar in clinical characteristics, who had been followed up for the 3 years prior to the pilot study. A questionnaire on the use of the telemedicine system showed a high degree of acceptance.

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the "On-Board" Concept

Type 1 diabetes mellitus (DM1) is a growing disease, and a deep understanding of the patient is required to prescribe the most appropriate treatment, adjusted to the patient's habits and characteristics. Before now, knowledge regarding each patient has been incomplete, discontinuous, and partial. However, the recent development of continuous glucose monitoring (CGM) and new biomedical sensors/gadgets, based on automatic continuous monitoring, offers a new perspective on DM1 management, since these innovative devices allow the collection of 24-hour biomedical data in addition to blood glucose levels. With this, it is possible to deeply characterize a diabetic person, offering a better understanding of his or her illness evolution, and, going further, develop new strategies to manage DM1. This new and global monitoring makes it possible to extend the "on-board" concept to other features. This well-known approach to the processing of variable "insulin" describes some inertias and aggregated/remaining effects. In this work, such analysis is carried out along with a thorough study of the significant variables to be taken into account/monitored-and how to arrange them-for a deep characterization of diabetic patients. Lastly, we present a case study evaluating the experience of the continuous and comprehensive monitoring of a diabetic patient, concluding that the huge potential of this new perspective could provide an acute insight into the patient's status and extract the maximum amount of knowledge, thus improving the DM1 management system in order to be fully functional.

The artificial pancreas: current status and future prospects in the management of diabetes

Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional-integral-derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.

The role of continuous glucose monitoring in the care of children with type 1 diabetes

Continuous glucose monitoring (CGM), while a relatively new technology, has the potential to transform care for children with type 1 diabetes. Some, but not all studies, have shown that CGM can significantly improve hemoglobin A1c levels and reduce time spent in the hypoglycemic range in children, particularly when used as part of sensor-augmented pump (SAP) therapy. Despite the publication of recent clinical practice guidelines suggesting CGM be offered to all children 8 years of age or older who are likely to benefit, and studies showing that younger children can also benefit, this technology is not yet commonly used by children with type 1 diabetes. Effects of CGM are enhanced when used on a near-daily basis (a use-dependent effect) and with insulin pump therapy. Therefore, coordinated strategies are needed to help children and their families initiate and continue to use this resource for diabetes care. This review introduces CGM to pediatric endocrinologists who are not yet familiar with the finer details of this technology, summarizes current data showing the benefits and limitations of CGM use in children, reviews specific case examples demonstrating when CGM can be helpful, and shows the value of both retrospective and real-time CGM. It is hoped that this information leads to discussion of this technology in pediatric endocrinology clinics as an important next step in improving the care of children with type 1 diabetes.

"Think different": a qualitative assessment of commercial innovation for diabetes information technology programs

BACKGROUND

This study reviews the state of diabetes information technology (IT) initiatives and presents a set of recommendations for improvement based on interviews with commercial IT innovators.

MATERIALS AND METHODS

Semistructured interviews were conducted with 10 technology developers, representing 12 of the most successful IT companies in the world. Average interview time was approximately 45 min. Interviews were audio-recorded, transcribed, and entered into ATLAS.ti for qualitative data analysis. Themes were identified through a process of selective and open coding by three researchers.

RESULTS

We identified two practices, common among successful IT companies, that have allowed them to avoid or surmount the challenges that confront healthcare professionals involved in diabetes IT development: (1) employing a diverse research team of software developers and engineers, statisticians, consumers, and business people and (2) conducting rigorous research and analytics on technology use and user preferences.

CONCLUSIONS

Because of the nature of their respective fields, healthcare professionals and commercial innovators face different constraints. With these in mind we present three recommendations, informed by practices shared by successful commercial developers, for those involved in developing diabetes IT programming: (1) include software engineers on the implementation team throughout the intervention, (2) conduct more extensive baseline testing of users and monitor the usage data derived from the technology itself, and (3) pursue Institutional Review Board-exempt research.

Italian contributions to the development of continuous glucose monitoring sensors for diabetes management

Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community.

Bringing the artificial pancreas home: telemedicine aspects

The design and implementation of telemedicine systems able to support the artificial pancreas need careful choices to cope with technological requirements while preserving performance and decision support capabilities. This article addresses the issue of designing a general architecture for the telemedicine components of an artificial pancreas and illustrates a viable solution that is able to deal with different use cases and is amenable to support mobile-health implementations. The goal is to enforce interoperability among the components of the architecture and guarantee maximum flexibility for the ensuing implementations. Thus, the design stresses modularity and separation of concerns along with adoption of clearly defined protocols for interconnecting the necessary components. This accounts for the implementation of integrated telemedicine systems suitable as short-term monitoring devices for supporting validation of closed-loop algorithms as well as devices meant to provide a lifelong tighter control on the patient state once the artificial pancreas has become the preferred treatment for patients with diabetes.