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.

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses.

BACKGROUND: Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. METHODS: A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. RESULTS: For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. CONCLUSIONS: The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day.

BACKGROUND: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. METHOD: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. RESULTS: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. CONCLUSIONS: The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

The Financial Impact of Inaccurate Blood Glucose Monitoring Systems.

OBJECTIVE: An in silico study of type 1 diabetes (T1DM) patients utilized the UVA-PADOVA Type 1 Diabetes Simulator to assess the effect of patient blood glucose monitoring (BGM) system accuracy on clinical outcomes. We applied these findings to assess the financial impact of BGM system inaccuracy. METHODS: The study included 43 BGM systems previously assessed for accuracy according to ISO 15197:2003 and ISO 15197:2013 criteria. Glycemic responses for the 100 in silico adult T1DM subjects were generated, using each meter. Changes in estimated HbA1c, severe hypoglycemic events, and health care resource utilization were computed for each simulation. The HbA1c Translator modeling approach was used to calculate the financial impact of these changes. RESULTS: The average cost of inaccuracy associated with the entire group of BGM systems was £155 per patient year (PPY). The average additional cost of BGM systems not meeting the ISO 15197:2003 standard was an estimated £178 PPY more than an average system that fulfills the standard and an estimated £235 PPY more than an average system that appears to meet the ISO 15197:2013 standard. CONCLUSION: There is a clear relationship between BGM system accuracy and cost, with the highest costs being associated with BGM systems not meeting the ISO 15197:2003 standard. Lower costs are associated with systems meeting the ISO 15197:2013 system accuracy criteria. Using BGM systems that meet the system accuracy criteria of the ISO 15197:2013 standard can help reduce the clinical and financial consequences associated with inaccuracy of BGM devices.

In Silico Modeling of Minimal Effective Insulin Doses Using the UVA/PADOVA Type 1 Diabetes Simulator.

BACKGROUND: The objective of this study was to identify the minimum basal insulin infusion rates and bolus insulin doses that would result in clinically relevant changes in blood glucose levels in the most insulin sensitive subjects with type 1 diabetes. METHODS: The UVA/PADOVA Type 1 Diabetes Simulator in silico population of children, adolescents, and adults was administered a basal insulin infusion rate to maintain blood glucose concentrations at 120 mg/dL (6.7 mmol/L). Two scenarios were modeled independently after 1 hour of simulated time: (1) basal insulin infusion rates in increments of 0.01 U/h were administered and (2) bolus doses in increments of 0.01 U were injected. Subjects were observed for 4 hours to determine insulin delivery required to change blood glucose by 12.5 mg/dL (0.7 mmol/L) and 25 mg/dL (1.4 mmol/L) in only 5% of the in silico population. RESULTS: The basal insulin infusion rates required to change blood glucose by 12.5 mg/dL and 25 mg/dL in 5% of children, adolescents, and adults were 0.03, 0.11, and 0.10 U/h and 0.06, 0.21, and 0.19 U/h, respectively. The bolus insulin doses required to change blood glucose by the target amounts in the respective populations were 0.10, 0.28, and 0.30 U and 0.19, 0.55, and 0.60 U. CONCLUSIONS: In silico modeling suggests that only a very small percentage of individuals with type 1 diabetes, corresponding to children with high insulin sensitivity and low body weight, will exhibit a clinically relevant change in blood glucose with very low basal insulin rate changes or bolus doses.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator.

BACKGROUND: Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia. METHOD: We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events. RESULTS: The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min). CONCLUSION: The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study.

BACKGROUND: Patients with diabetes rely on blood glucose (BG) monitoring devices to manage their condition. As some self-monitoring devices are becoming more and more accurate, it becomes critical to understand the relationship between system accuracy and clinical outcomes, and the potential benefits of analytical accuracy. METHODS: We conducted a 30-day in-silico study in type 1 diabetes mellitus (T1DM) patients using continuous subcutaneous insulin infusion (CSII) therapy and a variety of BG meters, using the FDA-approved University of Virginia (UVA)/Padova Type 1 Simulator. We used simulated meter models derived from the published characteristics of 43 commercial meters. By controlling random events in each parallel run, we isolated the differences in clinical performance that are directly associated with the meter characteristics. RESULTS: A meter's systematic bias has a significant and inverse effect on HbA1c ( P < .01), while also affecting the number of severe hypoglycemia events. On the other hand, error, defined as the fraction of measurements beyond 5% of the true value, is a predictor of severe hypoglycemia events ( P < .01), but in the absence of bias has a nonsignificant effect on average glycemia (HbA1c). Both bias and error have significant effects on total daily insulin (TDI) and the number of necessary glucose measurements per day ( P < .01). Furthermore, these relationships can be accurately modeled using linear regression on meter bias and error. CONCLUSIONS: Two components of meter accuracy, bias and error, clearly affect clinical outcomes. While error has little effect on HbA1c, it tends to increase episodes of severe hypoglycemia. Meter bias has significant effects on all considered metrics: a positive systemic bias will reduce HbA1c, but increase the number of severe hypoglycemia attacks, TDI use, and number of fingersticks per day.

Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study.

BACKGROUND: Standard management of type 1 diabetes (T1D) relies on blood glucose monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize BGM for calibration and dosing. The question of how the accuracy of both technologies interact is still not well understood. METHODS: We use a recently developed data-driven simulation approach to characterize the relationship between CGM and BGM accuracy especially how BGM accuracy impacts CGM performance in four different use cases with increasing levels of reliance on twice daily calibrated CGM. Simulations are used to estimate clinical outcomes and isolate CGM and BGM accuracy characteristics that drive performance. RESULTS: Our results indicate that meter (BGM) accuracy, and more specifically systematic positive or negative bias, has a significant effect on clinical performance (HbA1c and severe hypoglycemia events) in all use-cases generated for twice daily calibrated CGMs. Moreover, CGM sensor accuracy can amplify or mitigate, but not eliminate these effects. CONCLUSION: As a system, BGM and CGM and their mode of use (use-case) interact to determine clinical outcomes. Clinical outcomes (eg, HbA1c, severe hypoglycemia, time in range) can be closely approximated by linear relationships with two BGM accuracy characteristics, namely error and bias. In turn, the coefficients of this linear relationship are determined by the use-case and by CGM accuracy (MARD).

Treatment planning and image guidance for radiofrequency ablation of large tumors.

This article addresses the two key challenges in computer-assisted percutaneous tumor ablation: planning multiple overlapping ablations for large tumors while avoiding critical structures, and executing the prescribed plan. Toward semiautomatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, ranging from preoperative planning algorithms to an intraoperative execution platform. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation, which consists of optimal path selection and ablation coverage optimization based on integer programming. The system implementation is presented and validated in both phantom and animal studies. The presented system can potentially be further extended for other ablation techniques such as cryotherapy.

Needle-based interventions with the image-guided surgery toolkit (IGSTK): from phantoms to clinical trials.

We present three image-guided navigation systems developed for needle-based interventional radiology procedures, using the open source image-guided surgery toolkit (IGSTK). The clinical procedures we address are vertebroplasty, RF ablation of large lung tumors, and lung biopsy. In vertebroplasty, our system replaces the use of fluoroscopy, reducing radiation exposure to patient and physician. We evaluate this system using a custom phantom and compare the results obtained by a medical student, an interventional radiology fellow, and an attending physician. In RF ablation of large lung tumors, our system provides an automated interventional plan that minimizes damage to healthy tissue and avoids critical structures, in addition to accurate guidance of multiple electrode insertions. We evaluate the system's performance using an animal model. Finally, in the lung biopsy procedure, our system replaces the use of computed tomographic (CT) fluoroscopy, reducing radiation exposure to patient and physician, while at the same time enabling oblique trajectories which are considered challenging under CT fluoroscopy. This system is currently being used in an ongoing clinical trial at Georgetown University Hospital and was used in three cases.

Radiofrequency ablation of lung tumors in swine assisted by a navigation device with preprocedural volumetric planning.

PURPOSE: To develop an image guidance system that incorporates volumetric planning of spherical ablations and electromagnetic tracking of radiofrequency (RF) electrodes during insertion. MATERIALS AND METHODS: Simulated tumors were created in three live swine by percutaneously injecting agar nodules into the lung. A treatment plan was devised for each tumor with optimization software to solve the planning problem. The desired output was the minimum number of overlapping ablation spheres necessary to ablate each tumor and the margin. The insertion plan was executed with use of the electromagnetic tracking system that guided the insertion of the probe into precomputed locations. After a 72-hour survival period, animals were killed and histopathologic sections of the tissue were examined for cell viability and burn pattern analysis. RESULTS: A planning algorithm to spherically cover the tumors and the margin was computed. Electromagnetic tracking allowed successful insertion of the instrument, and impedance roll-off was reached in all ablations. Depending on their size, the tumors and the tumor margins were successfully covered with two to four ablation spheres. The image registration error was 1.0 mm +/- 0.64. The overall error of probe insertion was 9.4 mm +/- 3.0 (N = 8). Analysis of histopathologic sections confirmed successful ablations of the tissue. CONCLUSIONS: Computer-assisted RF ablation planning and electromagnetically tracked probe insertion were successful in three swine, validating the feasibility of electromagnetic tracking-assisted tumor targeting. Image misregistration caused by respiratory motion and tissue deformation contributed to the overall error of probe insertion.