Observer-based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay.

This article introduces an observer-based control strategy tailored for regulating plasma glucose in type 1 diabetes mellitus patients, addressing challenges like unknown time-varying delays and meal disturbances. This control strategy is based on an extended Bergman minimal model, a nonlinear glucose-insulin model to encompass unknown inputs, such as unplanned meals, exercise disturbances, or delays. The primary contribution lies in the design of an observer-based state feedback control in the presence of unknown long delays, which seeks to support and enhance the performance of the traditional artificial pancreas by considering realistic scenarios. The observer and control gains for the observer-based control are computed through linear matrix inequalities formulated from Lyapunov conditions that guarantee closed-loop stability. This design deploys a soft and gentle dynamic response, similar to a natural pancreas, despite meal disturbances and input delays. Numerical tests demonstrate the scheme's effectiveness in glycemic level regulation and hypoglycemic episode avoidance.

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.

Themes, Rates, and Risk of Adverse Events of the Artificial Pancreas in the United States Using MAUDE.

Three manufacturers sell artificial pancreas systems in the United States for management of Type 1 Diabetes. Given the life-saving task required of an artificial pancreas there needs to be a high level of trust and safety in the devices. This evaluation sought to find the adjusted safety event reporting rate and themes along with device-associated risk in events reported utilizing the MAUDE database. We searched device names in the MAUDE database over the period from 2016 until August 2023 (the date of retrieval). Thematic analysis was performed using dual-reviewer examination with a 96% concurrence. Relative risk (RR) was calculated for injury, malfunction, and overall, for each manufacturer, as well as adjusted event rate per manufacturer. Most events reported related to defects in the manufacturing of the casing materials which resulted in non-delivery of therapy. Tandem Diabetes Care, Inc. had an adjusted event rate of 50 per 100,000 units and RR of 0.0225. Insulet had an adjusted event rate of 300 per 100,000 units and RR of 0.1684. Medtronic has an adjusted event rate of 2771.43 per 100,000 units and RR of 20.7857. The newer Medtronic devices show improvements in likely event rate. While the artificial pancreas is still in its infancy, these event rates are not at an acceptable level for a device which can precipitate death from malfunctions. Further exploration into safety events and much more research and development is needed for devices to reduce the event rates. Improved manufacturing practices, especially the casing materials, are highly recommended. The artificial pancreas holds promise for millions but must be improved before it becomes a true life-saving device that it has the potential to become.

Parent, child, and adolescent lived experience using the insulin-only iLet Bionic Pancreas.

OBJECTIVE: Automated insulin delivery (AID) systems show great promise for improving glycemic outcomes and reducing disease burden for youth with type 1 diabetes (T1D). The current study examined youth and parent perspectives after using the insulin-only iLet Bionic Pancreas (BP) during the 13-week pivotal trial. METHODS: Parents and youth participated in focus group interviews, with questions assessing participants' experiences in a variety of settings and were grounded in the Unified Theory of Acceptance and Use of Technology. Qualitative analysis was completed by 3 authors using a hybrid thematic analysis approach. RESULTS: Qualitative analysis of focus groups revealed a total of 19 sub-themes falling into 5 major themes (Diabetes Burden, Freedom and Flexibility, Daily Routine, Managing Glucose Levels, and User Experience). Participants' overall experience was positive, with decreased burden and improved freedom and flexibility. Some participants reported challenges in learning to trust the system, adjusting to the user interface, and the system learning their body. CONCLUSION: This study adds to the growing literature on patient perspectives on using AID systems and was among the first to assess caregiver and youth experiences with the BP system over an extended period (13 weeks). Patient feedback on physical experiences with the device and experiences trusting the device to manage glucose should inform future development of technologies as well as approaches to education for patients and their families.

An intelligent wearable artificial pancreas patch based on a microtube glucose sensor and an ultrasonic insulin pump.

In order to improve the living standards of diabetes patients and reduce the negative health effects of this disease, the medical community has been actively searching for more effective treatments. In recent years, an artificial pancreas has emerged as an important approach to managing diabetes. Despite these recent advances, meeting the requirements for miniaturized size, accurate sensing and large-volume pumping capability remains a great challenge. Here, we present a novel miniaturized artificial pancreas based on a long microtube sensor integrated with an ultrasonic pump. Our device meets the requirements of achieving both accurate sensing and high pumping capacity. The artificial pancreas is constructed based on a long microtube that is low cost, painless and simple to operate, where the exterior of the microtube is fabricated as a glucose sensor for detecting diabetes and the interior of the microtube is used as a channel for delivering insulin through an ultrasonic pump. This work successfully achieved closed-loop control of blood glucose and treatment of diabetes in rats. It is expected that this work can open up new methodologies for the development of microsystems, and advance the management approach for diabetes patients.

Effects of an artificial pancreas on postoperative inflammation in patients with esophageal cancer.

PURPOSES: Subtotal esophagectomy for esophageal cancer (EC) is associated with high morbidity rates. Tight glycemic control using an artificial pancreas (AP) is one of the promising strategies to reduce postoperative inflammation and morbidities. However, the effects of tight glycemic control using AP in patients with EC are yet to be fully elucidated. METHOD: This study reviewed 96 patients with EC who underwent subtotal esophagectomy. The postoperative inflammation parameters and morbidity rates were compared between patients who used the AP (n = 27) or not (control group, n = 69). AP is a closed-loop system that comprises a continuous glucose monitor and an insulin pump. RESULTS: The numbers of white blood cells (WBC) and Neutrophils (Neut) were noted to be lower in the AP group than in the control group, but with no significant difference. The ratio in which the number of WBC, Neut, and CRP on each postoperative day (POD) was divided by those tested preoperatively was used to standardize the results. The ratio of WBC and Neut on 1POD was significantly lower in the AP group than in the control group. The rate of surgical site infection was lower in the AP group than in the control group. CONCLUSION: AP significantly decreased WBC and Neut on 1POD; this suggests the beneficial effects of AP in alleviating postoperative inflammation.

Recent advances in the precision control strategy of artificial pancreas.

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.

Background: Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm-a Neural-Net Artificial Pancreas (NAP)-an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm. Methods: The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22-68 years old, duration of diabetes 7-58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%-8.1%. Results: The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC. Conclusion: In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field. Clinical Trial Registration number: NCT05876273.

Time course changes in insulin sensitivity during cardiac surgery: A retrospective study on intraoperative glycemic management using an artificial pancreas.

INTRODUCTION: Glycemic control is essential for improving the prognosis of cardiac surgery, although precise recommendations have not yet been established. Under a constant blood glucose level, the insulin infusion rate correlates with insulin resistance during glycemic control using an artificial pancreas (AP). We conducted this retrospective study to elucidate changes in intraoperative insulin sensitivity as a first step to creating glycemic control guidelines. METHODS: Fifty-five cardiac surgery patients at our hospital who underwent intraoperative glycemic control using an AP were enrolled. Twenty-three patients undergoing surgical procedures requiring cardiac arrest under hypothermic cardiopulmonary bypass (CPB) with minimum rectal temperatures lower than 32°C, 13 patients undergoing surgical procedures requiring cardiac arrest under hypothermic CPB with minimum rectal temperatures of 32°C, eight patients undergoing on-pump beating coronary artery bypass grafting and 11 patients undergoing off-pump coronary artery bypass were assigned to groups A, B, C and D, respectively. We analyzed the time course of changes in the data derived from glycemic control using the AP. RESULTS: Significant time course changes were observed in groups A and B, but not in groups C and D. Insulin resistance was induced after the start of hypothermic CPB in groups A and B, and the induced change was not resolved by the rewarming procedure, remaining sustained until the end of surgery. CONCLUSIONS: Hypothermia is the predominant factor of the induced insulin resistance during cardiac surgery. Thus, careful glycemic management during hypothermic CPB is important. Prospective clinical studies are required to confirm the findings of this study.

Open-source Artificial Pancreas Systems Are Safe and Effective When Supported In-clinic: Outcomes in 248 Consecutive Type 1 Diabetes Clients.

OBJECTIVE: Our aim in this study was to determine the safety, glycemia, and quality of life (QoL) associated with in-clinic installation and management of supported open-source artificial pancreas systems (SOSAPS) in type 1 diabetes (T1D). METHODS: This investigation is a retrospective cohort study of consecutive SOSAPS users at a Canadian diabetes centre. SOSAPS were offered to all moderately tech-savvy T1D clients on sensor-augmented multiple daily injection or pump, able to pay for hardware, and willing to sign a consent and waiver document. SOSAPS were installed and maintained by clinic staff at no cost to clients. iPhone users were assigned to either Loop (n=108) or iPhone artificial pancreas systems (iAPS; n=114) and Android users to Android-type APS (n=24). Outcomes included severe hypoglycemia and diabetic ketoacidosis (DKA), time in range (TIR) 4.0 to 10.0 mmol/L, time below range (TBR) <4 mmol/L, glucose management indicator (GMI), mean sensor glucose (MSG), change in glycated hemoglobin (A1C), and QoL. RESULTS: Two hundred forty-eight subjects (131 males, 117 females), with a mean age of 36 years and diabetes duration of 21 years, experienced 3 episodes of severe hypoglycemia and no DKA over a follow-up of 17 months. TIR rose by 16%, from 64% to 80% (p<0.0001); TBR fell by 1.0%, from 3.5% to 2.5% (p=0.001); MSG fell from 9.0 to 8.1 mmol/L (p<0.001); GMI fell from 7.3% to 6.7% (p<0.001); and A1C fell from 7.2% to 6.7% (p<0.0001). QoL scores were healthy before and improved after SOSAPS. CONCLUSIONS: Clients with T1D using SOSAPS and supported with no-cost care to the client (software, technology, and physician/physician assistant) safely achieved improved TIR, GMI, A1C, and QoL.

12-Month Efficacy of Advanced Hybrid Closed-Loop System in Adult Type 1 Diabetes Patients.

Automated insulin delivery (AID) systems have improved glycemic control in individuals with type 1 diabetes (T1D). The "advanced hybrid closed loop" (AHCL) stands out as the most recent development in AID systems for T1D management. In a real-world clinical environment, we retrospectively evaluated the AHCL MiniMed™ 780G system's effectiveness to achieve and sustain glycemic control over a 12-month period in 22 adult T1D subjects. Within just 14 days of activating the automatic mode, the AHCL MiniMed 780G system showed rapid improvement in glycemic control, which persisted for 12 months. These findings underscore the effectiveness of AHCL systems in achieving and preserving optimal glycemic control in adults with T1D over a very long follow-up.

NIH Fifth Artificial Pancreas Workshop 2023: Meeting Report: The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption.

The Fifth Artificial Pancreas Workshop: Enabling Fully Automation, Access, and Adoption was held at the National Institutes of Health (NIH) Campus in Bethesda, Maryland on May 1 to 2, 2023. The organizing Committee included representatives of NIH, the US Food and Drug Administration (FDA), Diabetes Technology Society, Juvenile Diabetes Research Foundation (JDRF), and the Leona M. and Harry B. Helmsley Charitable Trust. In previous years, the NIH Division of Diabetes, Endocrinology, and Metabolic Diseases along with other diabetes organizations had organized periodic workshops, and it had been seven years since the NIH hosted the Fourth Artificial Pancreas in July 2016. Since then, significant improvements in insulin delivery have occurred. Several automated insulin delivery (AID) systems are now commercially available. The workshop featured sessions on: (1) Lessons Learned from Recent Advanced Clinical Trials and Real-World Data Analysis, (2) Interoperability, Data Management, Integration of Systems, and Cybersecurity, Challenges and Regulatory Considerations, (3) Adaptation of Systems Through the Lifespan and Special Populations: Are Specific Algorithms Needed, (4) Development of Adaptive Algorithms for Insulin Only and for Multihormonal Systems or Combination with Adjuvant Therapies and Drugs: Clinical Expected Outcomes and Public Health Impact, (5) Novel Artificial Intelligence Strategies to Develop Smarter, More Automated, Personalized Diabetes Management Systems, (6) Novel Sensing Strategies, Hormone Formulations and Delivery to Optimize Close-loop Systems, (7) Special Topic: Clinical and Real-world Viability of IP-IP Systems. "Fully automated closed-loop insulin delivery using the IP route," (8) Round-table Panel: Closed-loop performance: What to Expect and What are the Best Metrics to Assess it, and (9) Round-table Discussion: What is Needed for More Adaptable, Accessible, and Usable Future Generation of Systems? How to Promote Equitable Innovation? This article summarizes the discussions of the Workshop.

The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control.

INTRODUCTION: Diabetes mellitus type 1 is a chronic disease that implies mandatory external insulin delivery. The patients must monitor their blood glucose levels and administer appropriate insulin boluses to keep their blood glucose within the desired range. It requires a lot of time and endeavour, and many patients struggle with suboptimal glucose control despite all their efforts. MATERIALS AND METHODS: This narrative review combines existing knowledge with new discoveries from animal experiments. DISCUSSION: In the last decade, artificial pancreas (AP) devices have been developed to improve glucose control and relieve patients of the constant burden of managing their disease. However, a feasible and fully automated AP is yet to be developed. The main challenges preventing the development of a true, subcutaneous (SC) AP system are the slow dynamics of SC glucose sensing and particularly the delay in effect on glucose levels after SC insulin infusions. We have previously published studies on using the intraperitoneal space for an AP; however, we further propose a novel and potentially disruptive way to utilize the vasodilative properties of glucagon in SC AP systems. CONCLUSION: This narrative review presents two lesser-explored viable solutions for AP systems and discusses the potential for improvement toward a fully automated system: A) using the intraperitoneal approach for more rapid insulin absorption, and B) besides using glucagon to treat and prevent hypoglycemia, also administering micro-boluses of glucagon to increase the local SC blood flow, thereby accelerating SC insulin absorption and SC glucose sensor site dynamics.

Integration of a Safety Module to Prevent Rebound Hypoglycemia in Closed-Loop Artificial Pancreas Systems.

BACKGROUND: With automated insulin delivery (AID) systems becoming widely adopted in the management of type 1 diabetes, we have seen an increase in occurrences of rebound hypoglycemia or generated hypoglycemia induced by the controller's response to rapid glucose rises following rescue carbohydrates. Furthermore, as AID systems aim to enable complete automation of prandial control, algorithms are designed to react even more strongly to glycemic rises. This work introduces a rebound hypoglycemia prevention layer (HypoSafe) that can be easily integrated into any AID system. METHODS: HypoSafe constrains the maximum permissible insulin delivery dose based on the minimum glucose reading from the previous hour and the current glucose level. To demonstrate its efficacy, we integrated HypoSafe into the latest University of Virginia (UVA) AID system and simulated two scenarios using the 100-adult cohort of the UVA/Padova T1D simulator: a nominal case including three unannounced meals, and another case where hypoglycemia was purposely induced by an overestimated manual bolus. RESULTS: In both simulation scenarios, rebound hypoglycemia events were reduced with HypoSafe (nominal: from 39 to 0, hypo-induced: from 55 to 7) by attenuating the commanded basal (nominal: 0.27U vs. 0.04U, hypo-induced: 0.27U vs. 0.03U) and bolus (nominal: 1.02U vs. 0.05U, hypo-induced: 0.43U vs. 0.02U) within the 30-minute interval after treating a hypoglycemia event. No clinically significant changes resulted for time in the range of 70 to 180 mg/dL or above 180 mg/dL. CONCLUSION: HypoSafe was shown to be effective in reducing rebound hypoglycemia events without affecting achieved time in range when combined with an advanced AID system.

Effect of artificial pancreas system use on glycaemic control among pregnant women with type 1 diabetes mellitus: A meta-analysis of randomized controlled trials.

AIM: To assess the efficacy of artificial pancreas systems (APS) use among pregnant women with type 1 diabetes mellitus (T1DM) by conducting a meta-analysis. METHODS: We searched five databases, including EMBASE, Web of Science, PubMed, Cochrane Library and SCOPUS, for literature on APS use among pregnant women with T1DM before October 9, 2023. The primary endpoint was 24-hour time in range (TIR; 3.5-7.8 mmol/L). Secondary endpoints included glycaemic metrics for 24-hour (mean blood glucose [MBG], time above range [TAR], time below range [TBR]), and overnight TIR and TBR. RESULTS: We identified four randomized controlled trials involving 164 participants; one study with 16 participants focused on overnight APS use, and the other three focused on 24-hour APS use. Compared with standard care, APS exhibited a favourable effect on 24-hour TIR (standard mean difference [SMD] = 0.53, 95% confidence interval [CI] 0.25, 0.80, P < 0.001), overnight TIR (SMD = 0.67, 95% CI 0.39, 0.95, P < 0.001), and overnight TBR (<3.5 mmol/L; SMD = -0.49, 95% CI -0.77, -0.21 P < 0.001), while there was no significant difference in 24-hour TAR, 24-hour TBR, or MBG between the two groups. We further conducted subgroup analyses after removing the trial focused on overnight APS use and showed that 24-hour APS use reduced not only the 24-hour TIR (SMD = 0.41, 95% CI 0.12, 0.71; P = 0.007) but also the 24-hour TBR (<2.8 mmol/L; SMD = -0.77, 95% CI -1.32, -0.23, P = 0.006). CONCLUSION: Our findings suggest that APS might improve 24-hour TIR and overnight glycaemic control, and 24-hour APS use also significantly reduced 24-hour TBR (2.8 mmol/L) among pregnant women with T1DM.

Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas.

OBJECTIVE: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. METHOD: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. RESULTS: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. CONCLUSION: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. SIGNIFICANCE: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.

InsNET: Accurate Basal and Bolus Insulin Dose Prediction for Closed Loop Diabetes Management.

It has been demonstrated that closed-loop diabetes management results in better glycemic control and greater compliance than open-loop diabetes management. Deep learning models have been used to implement different components of artifical pancreas. In this work, a novel deep learning model InsNET has been proposed to estimate the basal and bolus insulin level and insulin bolus in patients with type I diabetes utilizing subcutaneous insulin infusion pumps for closed loop diabetes management system. The proposed InsNET is formed with a Wide-Deep combination of LSTM and GRU layers. Additionally, physical activity level has been included as an input in comparison to previous models where only past glucose levels (CGM), meal intake (CHO) and past insulin dosage were used as inputs. The proposed model was tested on In-silico data, and it achieved a Mean Absolute Error (MAE) of 0.002 and Root Mean Squared Error (RMSE) of 0.007 for UVA/Padova Dataset and MAE of 0.001 and RMSE OF 0.003 for mGIPsim Dataset.Clinical relevance- Insulin dose determination is an important as aspect of artificial pancreas. This work describes a deep learning model to determine accurate basal and bolus insulin dosage.

Controlling and powering a fully implantable artificial pancreas refillable by ingestible pills.

Over the past decade, there has been a growing interest in the development of an artificial pancreas for intraperitoneal insulin delivery. Intraperitoneal implantable pumps guarantee more physiological glycemic control than subcutaneous wearable ones, for the treatment of type 1 diabetes. In this work, a fully implantable artificial pancreas refillable by ingestible pills is presented. In particular, solutions enabling the communication between the implanted pump and external user interfaces and novel control algorithms to intraperitoneally release an adequate amount of insulin based on glycemic data are shown. In addition, the powering and the wireless battery recharging are addressed. Specifically, the design and optimization of a customized transcutaneous energy transfer with two independent wireless channels are presented. The system was tested in terms of recharging efficacy, possible temperature rise within the body, during the recharging process and reliability of the wireless connection in the air and in the presence of ex vivo tissues.Clinical Relevance- This work aims to improve the control, battery recharging, and wireless communication of a fully implantable artificial pancreas for type 1 diabetes treatment.