Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Design evaluation of a prototype user interface to support a guideline-based decision support system in gestational diabetes

Gestational Diabetes (GD) has increased over the last 20 years, affecting up to 15% of pregnant women worldwide. The complications associated can be reduced with the appropriate glycemic control during the pregnancy

Glucagon stimulation test to assess growth hormone status in Prader-Willi syndrome

Growth hormone deficiency (GHD) must be confirmed before starting treatment in adults with Prader-Willi syndrome (PWS). Most studies use the growth-hormone-releasing hormone plus arginine (GHRH-arginine) test. No data are available on the glucagon stimulation test (GST) in PWS. We compared the utility of fixed-dose (1 mg) GST versus GHRH-arginine test in diagnosing GHD. Adults and late adolescents with PWS underwent both tests on separate days. In the GHRH-arginine test, GHD was defined according to body mass index. In the GST, two cutoffs were analyzed: peak GH concentration 90 kg). We analyzed 34 patients: 22 weighing ≤ 90 kg and 12 weighing > 90 kg. In patients weighing ≤ 90 kg, the two tests were concordant in 16 (72.72%) patients (k = 0.476, p = 0.009 with GST cutoff 90 kg, the two tests were not concordant with GST cutoff 90 kg, the < 1 ng/mL cutoff seems better. Larger studies are necessary to establish definitive glucagon doses and cutoffs, especially in extremely obese patients

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%

Tour virtual: una forma eficiente de realizar evaluaciones tempranas en aplicaciones de paciente

Tradicionalmente, los sistemas de ayuda a la decisión (Decision Support Systems, DSS) han estado dirigidos a los profesionales médicos; sin embargo también pueden ayudar a aquellos pacientes que desean tener un papel más activo en el cuidado de su salud. Además, los pacientes quieren ser tratados en el momento en que su estado de salud lo requiera, sin importar el lugar en el que se encuentren. El sistema MobiGuide proporciona un soporte personalizado y basado en evidencia clínica tanto a profesionales médicos como a pacientes en todo momento y en todo lugar. La aplicación móvil del paciente representa el punto de acceso al servicio y, por tanto, es responsable en gran medida del éxito o fracaso del sistema. En MobiGuide, se ha incorporado a los pacientes desde el comienzo en el proceso de diseño y evaluación de la aplicación para garantizar una adecuada funcionalidad y usabilidad del sistema. En este trabajo presentamos la primera evaluación realizada por los pacientes mediante un tour virtual por la Aplicación de Paciente. Los resultados son altamente positivos y útiles para mejorar la aplicación, corregir defectos y conseguir la aplicación final esperada por los pacientes

A Fast 0.5 T Prepolarizer Module for Preclinical Magnetic Resonance Imaging

We present a magnet and high power electronics for Prepolarized Magnetic Resonance Imaging (PMRI) in a home-made, special-purpose preclinical system designed for simultaneous visualization of hard and soft biological tissues. The sensitivity of MRI systems grows with field strength, but so do their costs. PMRI can boost the signal-to-noise ratio (SNR) in affordable low-field scanners by means of a long and strong magnetic pulse. However, this must be rapidly switched off prior to the imaging pulse sequence, in timescales shorter than the spin relaxation (or T1) time of the sample. We have operated our prepolarizer at up to 0.5 T and demonstrated enhanced magnetization, image SNR and tissue contrast with PMRI of tap water, an ex vivo mouse brain and food samples. These have T1 times ranging from hundreds of milli-seconds to single seconds, while the preliminary high-power electronics setup employed in this work can switch off the prepolarization field in tens of milli-seconds. In order to make this system suitable for solid-state matter and hard tissues, which feature T1 times as short as 10 ms, we are developing new electronics which can cut switching times to ~ 300 μs. This does not require changes in the prepolarizer module, opening the door to the first experimental demonstration of PMRI on hard biological tissues

A Simulation Study of an Inverse Controller for Closed and Semiclosed-Loop Control in Type 1 Diabetes

Background: Closed-loop control algorithms in diabetes aim to calculate the optimum insulin delivery to maintain the patient in a normoglycemic state, taking the blood glucose level as the algorithm's main input. The major difficulties facing these algorithms when applied subcutaneously are insulin absorption time and delays in measurement of subcutaneous glucose with respect to the blood concentration. Methods: This article presents an inverse controller (IC) obtained by inversion of an existing mathematical model and validated with synthetic patients simulated with a different model and is compared with a proportional-integral-derivative controller. Results: Simulated results are presented for a mean patient and for a population of six simulated patients. The IC performance is analyzed for both full closed-loop and semiclosed-loop control. The IC is tested when initialized with the heuristic optimal gain, and it is compared with the performance when the initial gain is deviated from the optimal one (±10%). Conclusions: The simulation results show the viability of using an IC for closed-loop diabetes control. The IC is able to achieve normoglycemia over long periods of time when the optimal gain is used (63% for the full closed-loop control, and it is increased to 96% for the semiclosed-loop control

Portable magnetic resonance imaging of patients indoors, outdoors and at home

Mobile medical imaging devices are invaluable for clinical diagnostic purposes both in and outside healthcare institutions. Among the various imaging modalities, only a few are readily portable. Magnetic resonance imaging (MRI), the gold standard for numerous healthcare conditions, does not traditionally belong to this group. Recently, low-field MRI start-up companies have demonstrated the first decisive steps towards portability within medical facilities, but these are so far incompatible with more demanding use cases such as in remote and developing regions, sports facilities and events, medical and military camps, or home healthcare. Here we present in vivo images taken with a light, home-made, low-field extremity MRI scanner outside the controlled environment provided by medical facilities. To demonstrate the true portability of the system and benchmark its performance in various relevant scenarios, we have acquired images of a volunteer's knee in: i) an MRI physics laboratory; ii) an office room; iii) outside a campus building, connected to a nearby power outlet; iv) in open air, powered from a small fuel-based generator; and v) at the volunteer's home. All images have been acquired within clinically viable times, and signal-to-noise ratios (SNR) and tissue contrast suffice for 2D and 3D reconstructions with diagnostic value, with comparable overall image quality across all five situations. Furthermore, the volunteer carries a fixation metallic implant screwed to the femur, which leads to strong artifacts in standard clinical systems but appears sharp in our low-field acquisitions. Altogether, this work opens a path towards highly accessible MRI under circumstances previously unrealistic.Comment: 17 pages, 4 figures, comments welcom