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The purpose of this study was to develop and apply new physical heart defect models (PHDMs) that are patient-specific and color-coded with an optimized map.

Heart defect anatomies were segmented from medical images and reconstructed to form virtual models, which were then color-coded and rapid prototyped. The resulting PHDMs were used in a medical educational study to evaluate their pedagogical efficacy and in clinical case studies to investigate their utility in surgical planning.

A growing library of 36 PHDMs (including the most common defects) was generated. Results from the educational study showed that the PHDMs enabled uniquely effective learning, and the clinical case studies indicated that the models added value as surgical planning aids.

The education study involved a limited number of students, so future work should consider a larger sample size. The clinical case studies favored use of the PHDMs in surgical planning, but provided only qualitative support.

Workflow optimization is critical for PHDMs to be used effectively in surgical planning because some operations must be performed in emergently.

Because PHDMs have potential to influence surgeons’ actions as surgical planning aids, their use in that context must be thoroughly vetted.

The proposed models represent the first PHDMs that are patient-specific and fully color-coded with a standardized map optimized for the human visual system. The models enhanced medical education and facilitated effective surgical planning in this study.

The purpose of this study is the generation of a thorough generic heart model optimized for direct 3D printing with silicone elastomers.

The base of the model design is segmentation of CT data, followed by a generic adaption and a constructive enhancement. The model is 3D printed with silicone. An evaluation of the physical model gives indications about its benefits and weaknesses.

The results show the feasibility of a generic design while maintaining anatomical correctness and the benefit of the generic approach to quickly derive a multiplicity of healthy and pathological versions from one single model. The material properties of the silicone model are sufficient for simulation, but the results of the evaluation indicate possible improvements, as for most anatomical features, the used silicone is too hard and too stretchable.

Previous developments mostly focus on patient-specific heart models. In contrast, this study sets out to explore the possibility and benefits of a generic approach. Standardized validated models would allow comparability in surgical simulation.

Police-community relations are currently at a cross-road. Incidents over the past several years have severely damaged trust and faith in the police – particularly in minority communities. Society is faced with the choice of accepting an “us-vs-them” mentality with police on one side and citizens on the other or banding together to advance police-community coproduction in reducing violence. The purpose of this paper is to advance the latter by introducing a model for police to follow in police-citizen interactions in an effort to increase perceptions of fairness and legitimacy of police officers and police departments.

Using data from the National Police Research Platform’s Police-Community Interaction Survey, correlates of perceptions of fairness in police-community encounters as well as variation in agency-level fairness across 53 jurisdictions are examined.

Results show that application of the H.E.A.R.T. medical model is the most significant and substantial correlate of perceived fairness of police-community interactions and accounts for agency-level differences in perceived fairness.

The results highlight important ways that police can improve their image in the community and with minority communities in particular.

The cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.

The proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.

In the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.

The proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.

The high variability in cardiac surgery length – is one of the main challenges for staff managing productivity. This study aims to evaluate the impact of six interventions on open‐heart surgery operating theatre productivity.

A discrete operating theatre event simulation model with empirical operation time input data from 2,603 patients is used to evaluate the effect that these process interventions have on the surgery output and overtime work. A linear regression model was used to get operation time forecasts for surgery scheduling while it also could be used to explain operation time.

A forecasting model based on the linear regression of variables available before the surgery explains 46 per cent operating time variance. The main factors influencing operation length were type of operation, redoing the operation and the head surgeon. Reduction of changeover time between surgeries by inducing anaesthesia outside an operating theatre and by reducing slack time at the end of day after a second surgery have the strongest effects on surgery output and productivity. A more accurate operation time forecast did not have any effect on output, although improved operation time forecast did decrease overtime work.

A reduction in the operation time itself is not studied in this article. However, the forecasting model can also be applied to discover which factors are most significant in explaining variation in the length of open‐heart surgery.

The challenge in scheduling two open‐heart surgeries in one day can be partly resolved by increasing the length of the day, decreasing the time between two surgeries or by improving patient scheduling procedures so that two short surgeries can be paired.

A linear regression model is created in the paper to increase the accuracy of operation time forecasting and to identify factors that have the most influence on operation time. A simulation model is used to analyse the impact of improved surgical length forecasting and five selected process interventions on productivity in cardiac surgery.

We compare a recently proposed generalized eigensystem approach and a new modified generalized eigensystem approach to more widely used truncated singular value decomposition and zero‐order Tikhonov regularization for solving multidimensional elliptic inverse problems. As a test case, we use a finite element representation of a homogeneous eccentric spheres model of the inverse problem of electrocardiography. Special attention is paid to numerical issues of accuracy, convergence, and robustness. While the new generalized eigensystem methods are substantially more demanding computationally, they exhibit improved accuracy and convergence compared with widely used methods and offer substantially better robustness.

Predictive models implemented in medical procedures can potentially bring great benefit to patients and represent a step forward in targeted treatments based on a patient’s physiological condition. It is the purpose of this paper to outline such a model.

A multi-scale 0D-3D model based on patient specific geometry combines a 0-dimensional lumped parameter model (LPM) with a 3D computational fluid dynamics (CFD) analysis coupled in time, to obtain physiologically viable flow parameters.

A comparison of physiological data gathered from literature with flow-field measurements in this model shows the viability of this method in relation to potential predictions of pathological flows repercussions and candidate treatments.

A limitation of the model is the absence of compliance in the walls in the CFD fluid domain; however, compliance of the peripheral vasculature is accounted for by the LPM. Currently, an attempt is in progress to extend this multi-scale model to account for the fluid-structure interaction of the ventricular assist device vasculature and hemodynamics.

This work reports on a predictive pulsatile flow model that can be used to investigate surgical alternatives to reduce strokes in LVADs.

The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks (NNs), called multiple-valued logic neural networks (MVLNN), for predicting the behavior of phenomenological systems with highly nonlinear dynamics. MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics. MVLNNs possess some particular features, namely complex-valued weights, input, and outputs coded by kth roots of unity, and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces, which distinguish them from most of the existing NNs.

The presented study can be categorized into three sections. At the first part, the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN (AMVLNN). In this context, it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications. Besides, the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given. Through an analysis, some interesting characteristics of the Chua’s map, including a number of possible attractors of the state and sequences generated as a function of time, are given.

Based on a throughout simulation as well as a comprehensive numerical comparative study, some important features of AMVLNN are demonstrated. The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems. Furthermore, the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights. The results also unveil the potentials of AMVLNN as a fast, robust, and efficient control-oriented model at the heart of NMPC control schemes.

This study presents two innovative propositions. First, the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems. Second, the authors share the findings about the learning characteristics of MVLNNs. Through an exhaustive comparative study and considering different rival methodologies, a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs. Finally, the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end of the paper contains 748 references to papers, conference proceedings and theses/dissertations dealing with the finite element analyses and simulations in biomedicine that were published between 1985 and 1999.

This article draws on my doctoral research into the expansion of the Catholic educational mission in New Zealand in the years from 1945 to 1965. The project utilised archival and documentary sources and interviews with thirty three Catholic educators: twenty one female religious from the Sisters of Mercy, Dominican Sisters and the Religious of the Sacred Heart and twelve male religious from the Marist Brothers, Christian Brothers and the Society of Mary (Marist Priests) and two former diocesan directors of Catholic education.