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To achieve a fully automatic artificial pancreas (AP), i.e., an AP without the need for meal announcements, the intraperitoneal (IP) route is explored. This route has faster dynamics than the typical subcutaneous (SC) route. Model predictive control (MPC) is the most promising control algorithm, but it requires a predictive and identifiable model. This paper presents the design of such a model for MPC-based dual hormone IP APs. This model is trained and tested on recorded data from anesthetized pigs. Animal experiments show that the saturation of the hepatic first-pass effect is essential in how IP insulin and IP glucagon affect glucose levels. These physiological phenomena must be modeled to estimate the system behavior for various conditions. This, in turn, increases the number of parameters and complicates system identification. The availability of rich experimental data from 26 animal trials motivated the design of a technique to exploit this prior information to ensure the identifiability of our model. Through this technique, most parameters were either modeled as body weight functions or common among animals. The correlation between parameter values and body weight is discovered utilizing prior data from various animal experiments, such as blood glucose, plasma insulin, and glucagon levels, in which hormones were administered intraperitoneally or intravenously. This method simplifies the system identification for every new subject while keeping the model’s essential details that improve the prediction capability relative to comparable models. The model can be exploited in MPC or any other model-based controller of a bi-hormonal IP AP. It can also be used as a simulator to develop control approaches for single and bi-hormonal IP APs.

Aims: To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes. Material and methods: In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses. Results: Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose. Conclusions: The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change. Keywords: continuous glucose monitoring (CGM); type 1 diabetes.

Sex differences, tumor vascularity, and blood flow seen on multiecho dynamic contrast–based MRI scans enabled prediction of treatment response and overall survival in patients with rectal cancer. Background: MRI is the standard tool for rectal cancer staging. However, more precise diagnostic tests that can assess biologic tumor features decisive for treatment outcome are necessary. Tumor perfusion and hypoxia are two important features; however, no reference methods that measure these exist in clinical use. Purpose: To assess the potential predictive and prognostic value of MRI-assessed rectal cancer perfusion, as a surrogate measure of hypoxia, for local treatment response and survival. Materials and Methods: In this prospective observational cohort study, 94 study participants were enrolled from October 2013 to December 2017 (ClinicalTrials.gov: NCT01816607). Participants had histologically confirmed rectal cancer and underwent routine diagnostic MRI, an extended diffusion-weighted sequence, and a multiecho dynamic contrast agent–based sequence. Predictive and prognostic values of dynamic contrast-enhanced, dynamic susceptibility contrast (DSC), and intravoxel incoherent motion MRI were investigated with response to neoadjuvant treatment, progression-free survival, and overall survival as end points. Secondary objectives investigated potential sex differences in MRI parameters and relationship with lymph node stage. Statistical methods used were Cox regression, Student t test, and Mann-Whitney U test.Results: A total of 94 study participants (mean age, 64 years ± 11 [standard deviation]; 61 men) were evaluated. Baseline tumor blood flow from DSC MRI was lower in patients who had poor local tumor response to neoadjuvant treatment (96 mL/min/100 g ± 33 for ypT2–4, 120 mL/min/100 g ± 21 for ypT0–1; P = .01), shorter progression-free survival (hazard ratio = 0.97; 95% confidence interval: 0.96, 0.98; P < .001), and shorter overall survival (hazard ratio = 0.98; 95% confidence interval: 0.98, 0.99; P < .001). Women had higher blood flow (125 mL/min/100 g ± 27) than men (74 mL/min/100 g ± 26, P < .001) at stage 4. Volume transfer constant and plasma volume from dynamic contrast-enhanced MRI as well as ΔR2* peak and area under the curve for 30 and 60 seconds from DSC MRI were associated with local malignant lymph nodes (pN status). Median area under the curve for 30 seconds was 0.09 arbitrary units (au) ± 0.03 for pN1–2 and 0.19 au ± 0.12 for pN0 (P = .001). Conclusion:Low tumor blood flow from dynamic susceptibility contrast MRI was associated with poor treatment response in study participants with rectal cancer.

The continuous glucose monitoring (CGM) system is the most common system used by people with type 1 diabetes to monitor blood glucose levels. However, it measures glucose in interstitial fluid in subcutaneous tissue rather than directly in plasma. Measuring blood glucose level in this method has slow dynamics and introduce a time lag in capturing the blood glucose level. This can reduce the quality of blood glucose regulation and result in hypo- or hyperglycemia. In this paper, a linear Kalman filter is developed to predict blood glucose concentration using CGM data to compensate for that slow dynamics. To this end, an observable input-less model describing the glucose diffusion from plasma to interstitial fluid is utilized. Notably, this model is physiology-based, and its parameters can be obtained from the literature. The designed structure is evaluated on data from two animal experiments conducted on anesthetized pigs. The data sets include CGM measurements every 1.2 seconds and sporadic blood sample analysis during experiments. Results show that the designed approach sufficiently can compensate for the slow dynamics of CGM measurements when compared to blood glucose samples, and the performance is measured using statistical accuracy scores. This compensation can improve the decision-making of control algorithms for glucose regulation during rapid changes in glucose concentration, e.g., during meals and exercise.

Currently, continuous glucose monitoring sensors are used in the artificial pancreas to monitor blood glucose levels. However, insulin and glucagon concentrations in different parts of the body cannot be measured in real-time, and determining body glucagon sensitivity is not feasible. Estimating these states provides more information about the current system status, facilitating improved decision-making by the model-based controller. In this regard, the aim of this paper is to design a nonlinear high-gain observer for a bi-hormonal artificial pancreas in the presence of measurement noises, model uncertainties, and disturbances. The model used in the observer is based on an existing intraperitoneal nonlinear animal model in the literature. This model is modified by assuming that insulin can directly transfer from the peritoneal cavity to the bloodstream. Based on a set of realistic assumptions, one model is considered after each hormone infusion, and two observers are separately designed. The model is divided into the insulin-phase and glucagon-phase models based on a set of realistic assumptions. Thereafter, two high-gain observers are designed separately for these phases contributing to estimating the non-measurable states. The observer error is proven to be locally uniformly ultimately bounded, and it is verified that any asymptotically stable control laws remain stable in the presence of the observer. The performance of the observers with different gains is evaluated for a scenario with multiple insulin and glucagon infusions. The proposed observer converges to a finite error, according to the results. Clinical relevance- In Type 1 diabetic patients, the developed observer can be employed in a closed-loop artificial pan-creas to improve the performance of model-based controllers. It estimates the key states, which are necessary for forecasting the body's response to insulin and glucagon boluses.

The design of a model-based control method for an Artificial Pancreas requires a relatively simple and identifiable mathematical model to control glucose levels through hormone delivery. In this work we introduce new, simple nonlinear models to simulate data from experiments where insulin boluses are administrated in the peritoneal cavity. The models account for the delay between insulin administration and its nonlinear transport to other compartments. They were calibrated using experimental data from pigs. The results show that the suggested models are able to describe the data well, with average BIC value of 145. Moreover, the new models were compared with a common linear model which was not able to describe the data well, with BIC value of 920. They were also compared with a common nonlinear model which failed to represent insulin increases in the data and had BIC value of 637. Finally, profile likelihoods were applied for assessing the identifiability of one of the new models. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.

Objective: The design of an Artificial Pancreas (AP) to regulate blood glucose levels requires reliable control methods. Model Predictive Control has emerged as a promising approach for glycemia control. However, model--based control methods require computationally simple and identifiable mathematical models that represent glucose dynamics accurately, which is challenging due to the complexity of glucose homeostasis. Methods: In this work, a simple model is deduced to estimate blood glucose concentration in subjects with Type 1 Diabetes Mellitus (T1DM). Novel features in the model are power--law kinetics for intraperitoneal insulin absorption and a separate glucagon sensitivity state. Profile likelihood and a method based on singular value decomposition of the sensitivity matrix are carried out to assess parameter identifiability and guide a model reduction for improving the identification of parameters. Results: A reduced model with 10 parameters is obtained and calibrated, showing good fit to experimental data from pigs where insulin and glucagon boluses were delivered in the intraperitoneal cavity. Conclusion: A simple model with power--law kinetics can accurately represent glucose dynamics submitted to intraperitoneal insulin and glucagon injections. The reduced model was found to exhibit local practical as well as structural identifiability. Importance: The proposed model facilitates intraperitoneal bi-hormonal model-based closed-loop control in animal trials.

— An open source simulation model of the mechanical properties of a fully functional insulin pump was made in Matlab Simscape. The model simulates realistic behavior of an insulin pump, parts of which are validated against real-world systems. Simulations include mechanical forces and internal pressures, and the following fluid dynamics. Failure modes, such as occlusions, can be simulated and the resulting simulations can give new insights on how these failures affect the pump and how to detect them. Clinical relevance — Realistic pump simulations can be used to analyze how pump failures affect the system and in turn how to most effectively detect them before posing a hazard to the user, increasing the safety and reliability of the system.

Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of rather sudden bursts. Realistic recordings of abdominal sounds are contaminated with noise and artifacts from which the bowel sounds must be differentiated. Methods The proposed intrinsic mode function-fractal dimension (IMF-FD) filtering utilizes the property of the multivariate empirical mode decomposition (MEMD) to behave as a series of band pass filters. The MEMD decomposes the abdominal signal into its different frequency components. The resulting intrinsic mode functions (IMFs) are modulated in amplitude and frequency where transient sonic events occur. Based on the complexity of the IMFs, measured by their fractal dimension (FD) in sliding windows, the information-carrying IMFs are selected. The filtered signal is formed as the superposition of all selected IMFs. The IMF-FD filter not only enhances the non-linear components of the original signal but also segments them from the rest. Another important aspect of this work is that typical artifacts that occur in the same frequency range as bowel sounds can be subsequently eliminated by heuristic rules. Conclusions The method is tested on a realistic, contaminated data set with promising performance: close to 100% of the manually labeled bowel sounds are identified. © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

A typical artificial pancreas depends only on the continuous glucose monitoring (CGM) value for insulin dosing. However, both the insulin infusion and the glucose sensing are subject to time delays and slow dynamics. An automated and reliable meal onset information could enhance the control outcome of artificial pancreas by making it possible to infuse insulin earlier and thereby avoid large postprandial glucose excursions. In this study we employ abdominal sounds recorded in two healthy volunteers with a condenser microphone and propose an automated approach for meal onset detection from abdominal sounds. We use the Mel-frequency cepstral coefficients (MFCCs) and wavelet entropy extracted from the abdominal sounds as features. These features are fed to a simple feed forward neural network for discriminating meal from no-meal abdominal sounds. This approach detects meal onset with an average delay of 4.3 minutes in our limited number of subjects. More importantly, it provides lesser response delay than the state-of-the-art CGM based approach, which achieved a response delay ranging from 30- 40 minutes. The preliminary results indicate that the proposed abdominal sound-based approach may provide early meal onset information. This can be exploited in an artificial pancreas through allowable earlier meal insulin boluses, resulting in improved glycemic control. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Accurate continuous glucose monitoring (CGM) is essential for fully automated glucose control in diabetes mellitus type 1. State-of-the-art glucose control systems automatically regulate the basal insulin infusion. Users still need to manually announce meals to dose the prandial insulin boluses. An automated meal detection could release the user and improve the glucose regulation. In this study, patterns in the postprandial CGM data are exploited for meal detection. Binary classifiers are trained to recognize the postprandial pattern in horizons of the estimated glucose rate of appearance and in CGM data. The appearance rate is determined by moving horizon estimation (MHE) based on a simple model. Linear discriminant analysis (LDA) is used for classification. The proposed method is compared to methods that detect meals when thresholds are violated. Diabetes care data from twelve free-living pediatric patients was downloaded during regular screening. Experts identified meals and their start by retrospective evaluation. The classification was tested by cross-validation. Compared to the threshold-based methods, LDA showed higher sensitivity to meals with a low rate of false alarms. Classifying horizons outperformed the other methods also with respect to time of detection. The onset of meals can be detected by pattern recognition based on estimated model states and consecutive CGM measurements. No individual tuning is necessary. This makes the method easily adopted in the clinical practice. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

In classical approaches for an artificial pancreas, continuous glucose monitoring (CGM) is the only measured variable used for insulin dosing and additional control functions. The CGM values are subject to time delays and slow dynamics between blood and the sensing location. These time lags compromise the controller’s performance in maintaining (near to) normal glucose levels. Meal information could enhance the control outcome. However, meal announcement by the user is not reliable, and it takes 30 min to 40 min from meal onset until a meal is detected by methods based on CGM. In this pilot study, the use of bowel sounds for meal detection was investigated. In particular, we focused on whether bowel sounds change qualitatively during or shortly after meal ingestion. After fasting for at least 4 h, 11 healthy volunteers ingested a lunch meal at their usual time. Abdominal sound was recorded by a condenser microphone that was attached to the right upper quadrant of the abdomen by medical tape. Features that describe the power distribution over the frequency spectrum were extracted and used for classification by support vector machines. These classifiers were trained in a leave-one-out cross-validation scheme. Meals could be detected on average 10 min (std: 4.4 min) after they had started. Half of these were detected without false alarms. This shows that abdominal sound monitoring could provide an early meal detection. Further studies should investigate this possibility on a larger population in more general settings., Spectrogram of a recording in the training set. Windows with a length of 20 s and overlap of 10 s were analyzed using spectrogram function in MATLAB. The meal started at minute 51.

Glucose-insulin metabolism models are useful tools for research on diabetes, in development of diabetes-related medical devices like artificial pancreas systems, insulin pumps and continuous glucose monitors, and may also play a role in personalized decision support tools for people with diabetes. Such models are often highly nonlinear with many parameters that are person dependent. An example is the model used in the UVa/Padova T1DM simulator, which has a large number of states and parameters. It is desirable to be able to personalize such models through parameter identification based on limited glucose, meal and insulin data obtainable from free-living settings, as opposed to clinical research settings that have traditionally been required. In this paper we use the UVa-Padova T1DM simulator model in a case study to investigate observability of the model under different measurements, and the identifiability of its parameters as a function of the model's inputs and outputs. Structural identifiability is discussed and briefly investigated using the nonlinear Observability Rank Condition. Practical identifiability is discussed and investigated using sensitivity and Fisher information matrix analysis. We show how such analyses can be used to guide model reduction for improved identifiability, or to select the most proper subset of parameters to estimate. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Freestyle Libre (FL) is a factory calibrated Flash Glucose Monitor (FGM). We investigated Mean Absolute Relative Difference (MARD) between Self Monitoring of Blood Glucose (SMBG) and FL measurements in the first day of sensor wear in 39 subjects with Type 1 diabetes. The overall MARD was 12.3%, while the individual MARDs ranged from 4% to 25%. Five participants had a MARD &ge, 20%. We estimated bias and lag between the FL and SMBG measurements. The estimated biases range from &minus, 1.8 mmol / L to 1.4 mmol / L , and lags range from 2 min to 24 min . Bias is identified as a main cause of poor individual MARDs. The biases seem to persist in days 2&ndash, 7 of sensor usage. All cases of MARD &ge, 20% in the first day are eliminated by bias correction, and overall MARD is reduced from 12.3% to 9.2%, indicating that adding support for voluntary user-supplied bias correction in the FL could improve its performance.

The design of a prosthetic wrist is the result of compromises between the function and the practicality of the device. Conventional prosthetic wrists use a single degree of freedom to produce pro/supination of the hand. It has not been demonstrated that this is the most functional alignment for a single axis. Previous work by the authors suggests that if the wrist must have only one rotatory axis then a more oblique orientation would be more functional. To test this idea, a compact wrist with a single axis and spherical design has been made that will allow any axis of rotation to be selected and the functional performance of the resulting arm be tested. © 2012 – IOS Press and the authors. All rights reserved