Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem.

Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method.

Structural priors represented by discrete cosine transform improve EIT functional imaging.

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.

Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography.

Introduction: Coronavirus disease-2019 (COVID-19) pneumonia has different phenotypes. Selecting the patient individualized and optimal respirator settings for the ventilated patient is a challenging process. Electric impedance tomography (EIT) is a real-time, radiation-free functional imaging technique that can aid clinicians in differentiating the "low" (L-) and "high" (H-) phenotypes of COVID-19 pneumonia described previously.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.

Background: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics.

Methods: Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern.

Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.

Background: Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.

Detection of Different COVID-19 Pneumonia Phenotypes with Estimated Alveolar Collapse and Overdistention by Bedside Electrical Impedance Tomography.

COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which was reported to have different response and outcome to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the recruitability can help improve the patient outcome. In this contribution we conducted alveolar overdistention and collapse analysis with the long term electrical impedance tomography monitoring data on two severe COVID-19 pneumonia patients.

Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial.

Background: Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS.

Methods And Design: The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS.

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.

Background: The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm.

Effectiveness of Parameters in Quantifying Root Canal Morphology Change after Instrumentation with the Aid of a Microcomputed Tomography.

The objective of this study was to analyse the effectiveness of some parameters which characterise the change in morphology in human root canals subjected to ProTaper rotary enlargement with the help of an X-ray microfocus computed tomography (MCT) and to introduce a novel parameter that is effective in quantifying changes in root canal morphology. Ten each straight and curved root canals with mature apices chosen from extracted human upper incisor and canine teeth were scanned with MCT before and after canal shaping using ProTaper rotary instruments in order to facilitate three-dimensional digital reconstruction and quantitative gauging of relevant instrumental parameters and changes therein (surface area and volume). Root canal geometry change and the effectiveness of shaping were quantified with Structure Model Index change (ΔSMI) and surface area change to volume change ratio (ΔSA/ΔV).

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining.

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care.

Background: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia).

Objective: This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations.

Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis.

Background: The changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and hypoglycemia difficult. In addition, clinical practices can vary significantly, thus making GC protocols difficult to generalize across units.The aim of this study was to provide a retrospective analysis of the safety, performance and workload of the stochastic targeted (STAR) glycemic control (GC) protocol to demonstrate that patient-specific, safe, effective GC is possible with the STAR protocol and that it is also generalizable across/over different units and clinical practices.

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.

Background: Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment.

Implementation: CURE Soft is a desktop application developed in JAVA.

Daily evolution of insulin sensitivity variability with respect to diagnosis in the critically ill.

Introduction: This study examines the likelihood and evolution of overall and hypoglycemia-inducing variability of insulin sensitivity in ICU patients based on diagnosis and day of stay.

Materials And Methods: An analysis of model-based insulin sensitivity for n=390 patients in a medical ICU (Christchurch, New Zealand). Two metrics are defined to measure the variability of a patient's insulin sensitivity relative to predictions of a stochastic model created from the same data for all patients over all days of stay.

Pilot study of the SPRINT glycemic control protocol in a Hungarian medical intensive care unit.

Introduction: Stress-induced hyperglycemia increases morbidity and mortality. Tight control can reduce mortality but has proven difficult to achieve. The SPRINT (Specialized Relative Insulin and Nutrition Tables) protocol is the only protocol that reduced both mortality and hypoglycemia by modulating both insulin and nutrition, but it has not been tested in independent hospitals.

Identification of dental root canals and their medial line from micro-CT and cone-beam CT records.

Background: Shape of the dental root canal is highly patient specific. Automated identification methods of the medial line of dental root canals and the reproduction of their 3D shape can be beneficial for planning endodontic interventions as severely curved root canals or multi-rooted teeth may pose treatment challenges. Accurate shape information of the root canals may also be used by manufacturers of endodontic instruments in order to make more efficient clinical tools.

Efficient inhomogeneity compensation using fuzzy c-means clustering models.

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels.

Towards unified electromagnetic tracking system assessment-static errors.

Recent advances in Image-Guided Surgery allows physicians to incorporate up-to-date, high quality patient data in the surgical decision making, and sometimes to directly perform operations based on pre- or intra-operatively acquired patient images. Electromagnetic tracking is the fastest growing area within, where the position and orientation of tiny sensors can be determined with sub-millimeter accuracy in the field created by a generator. One of the major barriers to the wider spread of electromagnetic tracking solutions is their susceptibility to ferromagnetic materials and external electromagnetic sources.

Hypersensitivity to thromboxane receptor mediated cerebral vasomotion and CBF oscillations during acute NO-deficiency in rats.

Background: Low frequency (4-12 cpm) spontaneous fluctuations of the cerebrovascular tone (vasomotion) and oscillations of the cerebral blood flow (CBF) have been reported in diseases associated with endothelial dysfunction. Since endothelium-derived nitric oxide (NO) suppresses constitutively the release and vascular effects of thromboxane A(2) (TXA(2)), NO-deficiency is often associated with activation of thromboxane receptors (TP). In the present study we hypothesized that in the absence of NO, overactivation of the TP-receptor mediated cerebrovascular signaling pathway contributes to the development of vasomotion and CBF oscillations.

Differences in the laboratory parameters of obese and healthy Hungarian children and their use in automatic classification.

Obesity is a rapidly spreading endemic in almost every country of the developed world, of which Hungary is no exception. By a joint research project we aim to deepen our understanding of obesity-associated, and especially obesity-predicting changes of clinical markers (anthropometric indices, body composition, laboratory results etc.) in children, especially in teenage population.

Stochastic approach to error estimation for image-guided robotic systems.

Image-guided surgical systems and surgical robots are primarily developed to provide patient safety through increased precision and minimal invasiveness. Even more, robotic devices should allow for refined treatments that are not possible by other means. It is crucial to determine the accuracy of a system, to define the expected overall task execution error.

Stery-hand: A new device to support hand disinfection.

Incomplete disinfection can cause serious complications in surgical care. The teaching of effective hand washing is crucial in modern medical training. To support the objective evaluation of hand disinfection, we developed a compact, mobile device, relying on digital imaging and image processing.

Induced L₂-norm minimization of glucose-insulin system for Type I diabetic patients.

Using induced L₂-norm minimization, a robust controller was developed for insulin delivery in Type I diabetic patients. The high-complexity nonlinear diabetic patient Sorensen-model was considered and Linear Parameter Varying methodology was used to develop open-loop model and robust H(∞) controller. Considering the normoglycaemic set point (81.

Detection of the root canal's centerline from dental micro-CT records.

This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro CT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and segmentation, center point detection in the segmented slice, three dimensional inner surface reconstruction and morphological skeleton extraction in three dimensions. The central line of the root canal is interpolated as a 3D spline curve.