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Objective: Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.Methods: Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab(®)-a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.Results: The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4-6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1-4.6).Discussion: Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab(®), user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.Conclusion: Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia. [ABSTRACT FROM AUTHOR]

Background and Objective: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results.Methods: In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images.Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments.Conclusions: BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at: https://github.com/guomugong/BTS-DSN. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic shortest path approach to find an optimal sequence of tests to confirm or discard a disease, for any prescribed optimality criterion. The idea is to select the best sequence in which to apply a series of available tests, with a view at reaching a diagnosis with minimum expenditure of resources. The proposed approach derives an optimal policy whereby the decision maker is provided with a test strategy for each a priori probability of disease, aiming to reach posterior probabilities that warrant either immediate treatment or a not-ill diagnosis. [ABSTRACT FROM AUTHOR]

Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one. We convert the global calculation for the density as well as critical parameter in the process into local computations, and develop a binary tree structure to orderly store the neighbors for these local computations. Hence, the density computation turns out to be a direct access of the structure, rendering significantly computational saving. Performing experiments on synthetic point data and the JHU-DTI data set and comparing results of our fast DP algorithm and existing clustering methods, we can validate the efficiency and effectiveness of our fast DP algorithm. Finally, we demonstrate the application of the proposed algorithm on the analysis of pediatric WM tract development. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of introducing external medical review for disability insurance (DI) in a system relying on treating physician testimony for eligibility determination. Using a unique policy change and administrative data from Switzerland, I show that medical review reduces DI incidence by 23%. Incidence reductions are closely tied to difficult-to-diagnose conditions, suggesting inaccurate assessments by treating physicians. Due to a partial benefit system, reductions in full benefit awards are partly offset by increases in partial benefits. More intense screening also increases labor market participation. Existing benefit recipients are downgraded and lose part of their benefit income when scheduled medical reviews occur. Back-of-the-envelope calculations indicate that external medical review is highly cost-effective. Under additional assumptions, the results provide a lower bound of the effect on the false positive award error rate. [ABSTRACT FROM AUTHOR]

Background: The Weight-Specific Adolescent Instrument for Economic Evaluation (WAItE) is a new condition-specific patient reported outcome measure that incorporates the views of adolescents in assessing the impact of above healthy weight status on key aspects of their lives. Presently it is not possible to use the WAItE to calculate quality adjusted life years (QALYs) for cost-utility analysis (CUA), given that utility scores are not available for health states described by the WAItE.Objective: This paper examines different regression models for estimating Child Health Utility 9 Dimension (CHU-9D) utility scores from the WAItE for the purpose of calculating QALYs to inform CUA.Methods: The WAItE and CHU-9D were completed by a sample of 975 adolescents. Nine regression models were estimated: ordinary least squares, Tobit, censored least absolute deviations, two-part, generalized linear model, robust MM-estimator, beta-binomial, finite mixture models, and ordered logistic regression. The mean absolute error (MAE) and mean squared error (MSE) were used to assess the predictive ability of the models.Results: The robust MM-estimator with stepwise-selected WAItE item scores as explanatory variables had the best predictive accuracy.Conclusions: Condition-specific tools have been shown to be more sensitive to changes that are important to the population for which they have been developed for. The mapping algorithm developed in this study facilitates the estimation of health-state utilities necessary for undertaking CUA within clinical studies that have only collected the WAItE. [ABSTRACT FROM AUTHOR]

Abstract: Recently, we reported on the axial-shear strain fill-in of the interior of loosely bonded stiff elliptical inclusions in a soft backgroundat non-normal orientations, and the lack of fill-in in firmly bonded inclusions at any orientation. In this paper, we report on the experimental validation of the simulation studies using tissue-mimicking gelatin-based phantoms. We also show a few confirmatory examples of the existence of these phenomena in benign vs. malignant breast lesions in vivo. Phantom experiments showed that axial-shear strain zones caused by firmly bonded elliptical inclusions occurred only outside of the inclusion, as predicted by the simulation. By contrast, the axial-shear strain zones filled in the interior of loosely bonded elliptical inclusions at non-normal orientations. The axial-shear strain elastograms obtained from the in vivo cases appeared to be in general agreement with our experimental results. The results reported in this paper may have important clinical implications. Specifically, axial-shear strain fill-in inside an inclusion may be a unique signature of stiff, loosely bonded, ellipsoidal or elongated inclusions at non-normal orientations. Thus, it may be useful as a marker of benignity of benign breast lesions (e.g., fibroadenomas) that are generally stiff, elongated and loosely bonded to the host tissues. (E-mail: Jonathan.Ophir@uth.tmc.edu) [Copyright &y& Elsevier]

This paper discusses the potential of fuzzy logic methods within medical imaging. Technical advances have produced imaging techniques that can visualize structures and their functions in the living human body. The interpretation of these images plays a prominent role in diagnostic and therapeutic decisions, so physicians must deal with a variety of image processing methods and their applications. This paper describes three different sources of medical imagery that allow the visualization of nerve fibers in the human brain: (1) an algorithm for automatic segmentation of some parts of the thalamus in magnetic resonance images based on the differences in myelin content in various thalamic subnuclei; (2) polarized light for classifying the 3D orientation of the nerve fibers at each point; and (3) confocal laser scanning microscopy (CLSM) for calculating semiquantitative variables for myelin content. Fuzzy logic methods were applied to analyze these pictures from low- to high-level image processing. The solutions presented here are motivated by problems of routine neuroanatomic research demonstrating fuzzy-based methods to be valuable tools in medical image processing. [Copyright &y& Elsevier]

The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). The model is designed by cellular neural networks and it uses the proper features of electroencephalography. The results show that there are significant differences and abnormalities in the left hemisphere, (p < 0.05) at the electrodes AF3, F3, P7, T7, and O1 in the children with autism compared with the control group. Also, the evaluation of the obtained connections values between brain regions demonstrated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in children with autism. It is observed that the proposed model is able to distinguish the autistic children from the control subjects with an accuracy rate of 95.1% based on the obtained values of CNN using the SVM method. [ABSTRACT FROM AUTHOR]

Objective: To study the impact of genomic testing in shared decision making for men with clinically low-risk prostate cancer (PCa).Materials and Methods: Patients with clinically low-risk PCa were enrolled in a prospective, multi-institutional study of a validated 17-gene tissue-based reverse transcription polymerase chain reaction assay (Genomic Prostate Score [GPS]). In this paper we report on outcomes in the first 297 patients enrolled in the study with valid 17-gene assay results and decision-change data. The primary end points were shared decision on initial management and persistence on active surveillance (AS) at 1 year post diagnosis. AS utilization and persistence were compared with similar end points in a group of patients who did not have genomic testing (baseline cohort). Secondary end points included perceived utility of the assay and patient decisional conflict before and after testing.Results: One-year results were available on 258 patients. Shift between initial recommendation and shared decision occurred in 23% of patients. Utilization of AS was higher in the GPS-tested cohort than in the untested baseline cohort (62% vs 40%). The proportion of men who selected and persisted on AS at 1 year was 55% and 34% in the GPS and baseline cohorts, respectively. Physicians reported that GPS was useful in 90% of cases. Mean decisional conflict scores declined in patients after GPS testing.Conclusion: Patients who received GPS testing were more likely to select and persist on AS for initial management compared with a matched baseline group. These data indicate that GPS help guide shared decisions in clinically low-risk PCa. [ABSTRACT FROM AUTHOR]

Introduction: Excisional surgical debridement (SD) is still the gold standard in the treatment of deeply burned hands, though the intricate anatomy is easily damaged. Previous studies demonstrated that enzymatic debridement with the bromelain debriding agent NexoBrid® (EDNX) is more selective and thus can preserve viable tissue with excellent outcome results. So far no method paper has been published presenting different treatment algorithms in this new field. Therefore our aim was to close this gap by presenting our detailed learning curve in EDNX of deeply burned hands.Methods: We conducted a single-center prospective observational clinical trial treating 20 patients with deeply burned hands with EDNX. Different anaesthetic procedures, debridement and wound treatment algorithms were compared and main pitfalls described.Results: EDNX was efficient in 90% of the treatments though correct wound bed evaluation was challenging and found unusual compared to SD. Post EDNX surprisingly the majority of the burn surface area was found overestimated (18 wounds). Finally we simplified our process and reduced treatment costs by following a modified treatment algorithm and treating under plexus anaesthesia bedside through a single nurse and one burn surgeon solely. Suprathel® could be shown to be an appropriate dressing for wound treatment after EDNX. Complete healing (less 5% rest defect) was achieved at an average of day 28.Conclusion: EDNX in deep burned hands is promising regarding handling and duration of the treatment, efficiency and selectivity of debridement, healing potential and early rehabilitation. Following our treatment algorithm EDNX can be performed easily and even without special knowledge in burn wound depth evaluation. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered. A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test. The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR. [ABSTRACT FROM AUTHOR]

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings. This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Kilovoltage intrafraction monitoring (KIM) is a new real-time 3-dimensional image guidance method. Unlike previous real-time image guidance methods, KIM uses a standard linear accelerator without any additional equipment needed. The first prospective clinical trial of KIM is underway for prostate cancer radiation therapy. In this paper we report on the measured motion accuracy and precision using real-time KIM-guided gating.Methods and Materials: Imaging and motion information from the first 200 fractions from 6 patient prostate cancer radiation therapy volumetric modulated arc therapy treatments were analyzed. A 3-mm/5-second action threshold was used to trigger a gating event where the beam is paused and the couch position adjusted to realign the prostate to the treatment isocenter. To quantify the in vivo accuracy and precision, KIM was compared with simultaneously acquired kV/MV triangulation for 187 fractions.Results: KIM was successfully used in 197 of 200 fractions. Gating events occurred in 29 fractions (14.5%). In these 29 fractions, the percentage of beam-on time, the prostate displacement was >3 mm from the isocenter position, reduced from 73% without KIM to 24% with KIM-guided gating. Displacements >5 mm were reduced from 16% without KIM to 0% with KIM. The KIM accuracy was measured at <0.3 mm in all 3 dimensions. The KIM precision was <0.6 mm in all 3 dimensions.Conclusions: Clinical implementation of real-time KIM image guidance combined with gating for prostate cancer eliminates large prostate displacements during treatment delivery. Both in vivo KIM accuracy and precision are well below 1 mm. [ABSTRACT FROM AUTHOR]

Two-step floating catchment area (2SFCA) techniques are popular for measuring potential geographical accessibility to health care services. This paper proposes methodological enhancements to increase the sophistication of the 2SFCA methodology by incorporating both public and private transport modes using dedicated network datasets. The proposed model yields separate accessibility scores for each modal group at each demand point to better reflect the differential accessibility levels experienced by each cohort. An empirical study of primary health care facilities in South Wales, UK, is used to illustrate the approach. Outcomes suggest the bus-riding cohort of each census tract experience much lower accessibility levels than those estimated by an undifferentiated (car-only) model. Car drivers' accessibility may also be misrepresented in an undifferentiated model because they potentially profit from the lower demand placed upon service provision points by bus riders. The ability to specify independent catchment sizes for each cohort in the multi-modal model allows aspects of preparedness to travel to be investigated. [ABSTRACT FROM AUTHOR]

Purpose: Explore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs.Methods and Materials: This paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a "What-if analysis" shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine - St. George's Hospital, London, period 1969-1984, and January 2000.Results: The hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (£755 vs. £1172). Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (£134 vs. £947).Conclusion: Encoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization. [ABSTRACT FROM AUTHOR]

Objective: This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video.Methods and Materials: To select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity.Results: For experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency.Conclusions: For gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer-aided diagnosis or medical report generation. [ABSTRACT FROM AUTHOR]

Objective: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. Gene data is usually composed of just a few dozen objects described by thousands of features. For this kind of data, it is easy to find a model that fits the learning data. However, it is not easy to find one that will simultaneously evaluate new data equally well as learning data. This overfitting issue is well known as regards classification and regression, but it also applies to feature selection.Methods and Materials: We address this problem and investigate its importance in an empirical study of four feature selection methods applied to seven high-dimensional gene datasets. We chose datasets that are well studied in the literature-colon cancer, leukemia and breast cancer. All the datasets are characterized by a significant number of features and the presence of exactly two decision classes. The feature selection methods used are ReliefF, minimum redundancy maximum relevance, support vector machine-recursive feature elimination and relaxed linear separability.Results: Our main result reveals the existence of positive feature selection bias in all 28 experiments (7 datasets and 4 feature selection methods). Bias was calculated as the difference between validation and test accuracies and ranges from 2.6% to as much as 41.67%. The validation accuracy (biased accuracy) was calculated on the same dataset on which the feature selection was performed. The test accuracy was calculated for data that was not used for feature selection (by so called external cross-validation).Conclusions: This work provides evidence that using the same dataset for feature selection and learning is not appropriate. We recommend using cross-validation for feature selection in order to reduce selection bias. [ABSTRACT FROM AUTHOR]

Objective: To compare the Diabetes-39 (D-39) with six multi-attribute utility (MAU) instruments (15D, AQoL-8D, EQ-5D, HUI3, QWB, and SF-6D), and to develop mapping algorithms which could be used to transform the D-39 scores into the MAU scores.Research Design and Methods: Self-reported diabetes sufferers (N=924) and members of the healthy public (N=1760), aged 18 years and over, were recruited from 6 countries (Australia 18%, USA 18%, UK 17%, Canada 16%, Norway 16%, and Germany 15%). Apart from the QWB which was distributed normally, non-parametric rank tests were used to compare subgroup utilities and D-39 scores. Mapping algorithms were estimated using ordinary least squares (OLS) and generalised linear models (GLM).Results: MAU instruments discriminated between diabetes patients and the healthy public; however, utilities varied between instruments. The 15D, SF-6D, AQoL-8D had the strongest correlations with the D-39. Except for the HUI3, there were significant differences by gender. Mapping algorithms based on the OLS estimator consistently gave better goodness-of-fit results. The mean absolute error (MAE) values ranged from 0.061 to 0.147, the root mean square error (RMSE) values 0.083 to 0.198, and the R-square statistics 0.428 and 0.610. Based on MAE and RMSE values the preferred mapping is D-39 into 15D. R-square statistics and the range of predicted utilities indicate the preferred mapping is D-39 into AQoL-8D.Conclusions: Utilities estimated from different MAU instruments differ significantly and the outcome of a study could depend upon the instrument used. The algorithms reported in this paper enable D-39 data to be mapped into utilities predicted from any of six instruments. This provides choice for those conducting cost-utility analyses. [ABSTRACT FROM AUTHOR]

Abstract: Image reconstruction is central to many scientific fields, from medical ultrasound and sonar to computed tomography and computer vision. Although lenses play a critical reconstruction role in these fields, digital sensors enable more sophisticated computational approaches. A variety of computational methods have thus been developed, with the common goal of increasing contrast and resolution to extract the greatest possible information from raw data. This paper describes a new image reconstruction method named the Diffuse Time-domain Optimized Near-field Estimator (dTONE). dTONE represents each hypothetical target in the system model as a diffuse region of targets rather than a single discrete target, which more accurately represents the experimental data that arise from signal sources in continuous space, with no additional computational requirements at the time of image reconstruction. Simulation and experimental ultrasound images of animal tissues show that dTONE achieves image resolution and contrast far superior to those of conventional image reconstruction methods. We also demonstrate the increased robustness of the diffuse target model to major sources of image degradation through the addition of electronic noise, phase aberration and magnitude aberration to ultrasound simulations. Using experimental ultrasound data from a tissue-mimicking phantom containing a 3-mm-diameter anechoic cyst, the conventionally reconstructed image has a cystic contrast of –6.3 dB, whereas the dTONE image has a cystic contrast of –14.4 dB. (E-mail: mae3x@virginia.edu) [Copyright &y& Elsevier]

Abstract: Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’ by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature. [Copyright &y& Elsevier]

Abstract: Matrix-phased array transducers for real-time 3-D ultrasound enable fast, noninvasive visualization of cardiac ventricles. Typically, 3-D ultrasound images are semiautomatically segmented to extract the left ventricular endocardial surface at end-diastole and end-systole. Automatic segmentation and propagation of this surface throughout the entire cardiac cycle is a challenging and cumbersome task. If the position of the endocardial surface is provided at one or two time frames during the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of 3-D ultrasound data. In this paper, we applied a region-based tracking algorithm to track the endocardial surface between two reference frames that were manually segmented. To evaluate the tracking of the endocardium, the method was applied to 40 open-chest dog datasets with 484 frames in total. Ventricular geometry and volumes derived from region-based endocardial surfaces and manual tracing were quantitatively compared, showing strong correlation between the two approaches. Statistical analysis showed that the errors from tracking were within the range of interobserver variability of manual tracing. Moreover, our algorithm performed well on ischemia datasets, suggesting that the method is robust-to-abnormal wall motion. In conclusion, the proposed optical flow-based surface tracking method is very efficient and accurate, providing dynamic “interpolation” of segmented endocardial surfaces. (E-mail: qd2002@columbia.edu) [Copyright &y& Elsevier]

Abstract: Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws’ texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke. (E-mail: knikita@cc.ece.ntua.gr) [Copyright &y& Elsevier]

Abstract: This paper presents an in-vivo study of synthetic transmit aperture (STA) imaging in comparison with conventional imaging, evaluating whether STA imaging is feasible in-vivo and whether the image quality obtained is comparable with traditional scanned imaging in terms of penetration depth, spatial resolution, contrast resolution and artifacts. Acquisition was performed using our research scanner RASMUS and a 5.5 MHz convex array transducer. STA imaging was acquired using circular wave emulation by 33-element subapertures and a 20 μs linear FM signal as excitation pulse. For conventional imaging, a 64 element aperture was used in transmit and receive with a 1.5 cycle sinusoid excitation pulse. Conventional and STA images were acquired interleaved, ensuring that the exact same anatomical location was scanned. Image sequences were recorded in real time and processed off-line. Seven male volunteers were scanned abdominally and the resulting images were compared by three medical doctors using randomized blinded presentation. Penetration and image quality were scored and evaluated statistically. Results showed slightly but significantly (0.48 cm, p = 0.008) increased penetration using STA. Image quality was also highly significantly (p < 0.001) increased. Results show that in-vivo ultrasound imaging using STA is feasible for abdominal imaging without severe motion artifacts. (E-mail: mhp@dadlnet.dk) [Copyright &y& Elsevier]

Rationale and Objectives: Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum.Materials and Methods: This paper presents a fully automatic method for the three-dimensional smoothing of the lung boundary using information from the segmented human airway tree. First, using the segmented airway tree, we define a bounding box around the mediastinum for each lung, within which all operations are performed. We then define all generations of the airway tree distal to the right and left main stem bronchi to be part of the respective lungs and exclude all other segments. Finally, we perform a fast morphological closing with an ellipsoidal kernel to smooth the surface of the lung.Results: This method has been tested by processing the segmented lungs from eight normal datasets. The mean value of the magnitude of curvedness of the lung contours, averaged over all the datasets, is 0.13 postsmoothing, compared with 4.91 before smoothing. The accuracy of the lung contours after smoothing is assessed by comparing the automatic results to manually traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square difference between human and computer borders is 0.8691 mm after smoothing, compared with 1.3012 mm before smoothing. The mean similarity index, which is an area overlap measure based on the kappa statistic, is 0.9958 (SD 0.0032), after smoothing.Conclusions: We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Because there are no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for three-dimensional smoothing. [ABSTRACT FROM AUTHOR]

Purpose: Recent imaging studies have demonstrated that there can be significant changes in anatomy from day to day and over the course of radiotherapy as a result of daily positioning uncertainties and physiologic and clinical factors. There are a number of strategies to minimize such changes, reduce their impact, or correct for them. Measures to date have included improved immobilization of external and internal anatomy or adjustment of positions based on portal or ultrasound images. Perhaps the most accurate way is to use CT image–guided radiotherapy, for which the possibilities range from simple correction of setup based on daily CT images to on-line near real-time intensity modulated radiotherapy (IMRT) replanning. In addition, there are numerous intermediate possibilities. In this paper, we report the development of one such intermediate method that takes into account anatomic changes by deforming the intensity distributions of each beam based on deformations of anatomy as seen in the beam''s-eye–view. Methods and materials: The intensity distribution deformations are computed based on anatomy deformations discerned from the changes in the current image relative to a reference image (e.g., the pretreatment CT scan). First, a reference IMRT plan is generated based on the reference CT image. A new CT image is acquired using an in-room CT for every fraction. The anatomic structure contours are obtained for the new image. (For this article, these contours were manually drawn. When image guided IMRT methods are implemented, anatomic structure contours on subsequent images will likely be obtained with automatic or semiautomatic means. This could be achieved by, for example, first deforming the original CT image to match today''s image, and then using the same deformation transformation to map original contours to today''s image.) The reference intensity distributions for each beam are then deformed so that the projected geometric relationship within the beam''s-eye–view between the anatomy (both target and normal tissues) extracted from the reference image and the reference intensity distribution is the same as (or as close as possible to) the corresponding relationship between anatomy derived from today''s image and the newly deformed intensity distributions. To verify whether the dose distributions calculated using the deformed intensity distributions are acceptable for treatment as compared to the original intensity distributions, the deformed intensities are transformed into leaf sequences, which are then used to compute intensity and dose distributions expected to be delivered. The corresponding dose–volume histograms and dose–volume and dose–response indices are also computed. These data are compared with the corresponding data derived (a) from the original treatment plan applied to the original image, (b) from the original treatment plan applied to today''s image, and (c) from a new full-fledged IMRT plan designed based on today''s image. Results: Depending on the degree of anatomic changes, the use of an IMRT plan designed based on the original planning CT for the treatment of the current fraction could lead to significant differences compared to the intended dose distributions. CT-guided setup compared to the setup based on skin marks or bony landmarks may improve dose distributions somewhat. Replanning IMRT based on the current fraction''s image yields the best physically deliverable plan (the “gold standard”). For the prostate and head-and-neck examples studied as proof of principle, the results of deforming intensities within each beam based on the anatomy seen in the beam''s-eye–view are a good approximation of full-fledged replanning compared with other alternatives. Conclusions: Our preliminary results encourage us to believe that deforming intensities taking into account deformation in the anatomy may be a rapid way to produce new treatment plans on-line in near real-time based on daily CT images. The methods we have developed need to be applied to a group of patients for both prostate and head-and-neck cases to confirm the validity of our approach. [Copyright &y& Elsevier]

The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one. [Copyright &y& Elsevier]

: PurposeWith the advent of intraoperative optimized planning, the treatment of prostate cancer with permanent implants has reached an unprecedented level of dose conformity. However, because of well-documented (and unavoidable) inaccuracies in seed placement into the gland, carrying out a plan results in a large degree of variability relative to the intended dose distribution. This brings forth the need to periodically readjust the plan to allow for the real positions of seeds already implanted. In this paper, an algorithm for performing this task, hereby described as intraoperative dynamic dose optimization (IDDO), is presented and assessed.: Methods and materialsThe general scheme for performing IDDO consists of three steps: (1) at some point during the implant, coordinates of implanted seeds are identified; (2) seed images are projected onto the reference frame of the ultrasound images for planning; and (3) the plan is reoptimized. Work on the first two steps is reported elsewhere. Here, we focus on the strategy for implementing the reoptimization step. An optimal treatment plan is first obtained based on initial operating room–acquired ultrasound images. We analyze the sensitivity and effect of the IDDO procedure with respect to the total number of reoptimizations performed. Specifically, we consider reoptimizing 2, 3, and 4 times. When two reoptimizations are used, half of the seeds from the initial optimal plan are implanted. The first reoptimization is performed on the remaining possible seed positions, and all the seeds designated in this reoptimized plan are implanted. The second (final) reoptimization is done on the remaining unused seed positions to ensure 100% coverage of the gland and to eliminate possible cold spots in the gland. Similarly, when three reoptimization steps are used, one-third of the seeds from the initial optimized plan, one-half of the seeds from the first reoptimization, and all seeds from the second reoptimization are implanted. The third (final) reoptimization is performed to assist in eliminating possible cold spots. Reoptimizing four times proceeds in a like manner. Fifteen patient cases are used for comparison. Strict dose bounds of 100% and 120% of the prescription dose are imposed on the urethra, and 100% coverage is imposed on the prostate volume. To assist in achieving good conformity, prostate contour points are assigned a target upper dose bound of 150% of the prescription dose.: ResultsA two-way comparison is performed: (a) initial optimized plan, (b) IDDO plan. Postimplant dose analysis, coverage and conformity measures, as well as actual dose received by urethra and rectum are used to gauge the results. The initial optimized plan consistently provides 93% prescription dose coverage to the gland with average conformity index of 1.32. The urethra dose ranges within 100% to 150%, and the maximum dose delivered to the rectum reaches 91% of the prescription dose. On average, about 50% of the urethra receives more than 120% of the prescription dose, and 19% of the rectum volume receives more than the 78% upper dose limit. For the IDDO plan, 100% postimplant coverage with 1.16 conformity is achieved. Urethra and rectum dose is maintained within the prescribed 100% to 120% range and 78% upper bound, respectively.: ConclusionsWith real-time treatment planning, it is possible to dynamically reoptimize treatment plans to account for actual seed positions (as opposed to planned positions) and needle-induced swelling to the gland during implantation. Postimplant analysis shows that the final seed configuration resulting from the IDDO method yields improved dosimetry. The algorithmic design ensures that one can achieve complete coverage while maintaining good conformity, thus sparing excess radiation to external tissue. The study also provides evidence of the possibility of morbidity reduction to urethra and rectum (because of reduced dose delivered to these structures) via the use of IDDO planning. Clinical studies are needed to validate the importance of our approach. [Copyright &y& Elsevier]

In this study, a Bayesian approach was used for 3-D reconstruction in the presence of multiplicative noise and nonlinear compression of the ultrasound (US) data. Ultrasound images are often considered as being corrupted by multiplicative noise (speckle). Several statistical models have been developed to represent the US data. However, commercial US equipment performs a nonlinear image compression that reduces the dynamic range of the US signal for visualization purposes. This operation changes the distribution of the image pixels, preventing a straightforward application of the models. In this paper, the nonlinear compression is explicitly modeled and considered in the reconstruction process, where the speckle noise present in the radio frequency (RF) US data is modeled with a Rayleigh distribution. The results obtained by considering the compression of the US data are then compared with those obtained assuming no compression. It is shown that the estimation performed using the nonlinear log-compression model leads to better results than those obtained with the Rayleigh reconstruction method. The proposed algorithm is tested with synthetic and real data and the results are discussed. The results have shown an improvement in the reconstruction results when the compression operation is included in the image formation model, leading to sharper images with enhanced anatomical details. [ABSTRACT FROM AUTHOR]

Several techniques have been described in the literature in recent years for the reconstruction of a regular volume out of a series of ultrasound (US) slices with arbitrary orientations, typically scanned by means of US freehand systems. However, a systematic approach to such a problem is still missing. This paper focuses on proposing a theoretical framework for the 3-D US volume reconstruction problem. We introduce a statistical method for the construction and trimming of the sampling grid where the reconstruction will be carried out. The results using in vivo US data demonstrate that the computed reconstruction grid that encloses the region-of-interest (ROI) is smaller than those obtained from other reconstruction methods in those cases where the scanning trajectory deviates from a pure straight line. In addition, an adaptive Gaussian interpolation technique is studied and compared with well-known interpolation methods that have been applied to the reconstruction problem in the past. We find that the proposed method numerically outperforms former proposals in several control studies; subjective visual results also support this conclusion and highlight some potential deficiencies of methods previously proposed. [ABSTRACT FROM AUTHOR]

This paper demonstrates the feasibility of the automation of forensic hair analysis and comparison task using neural network explanation systems (NNESs). Our system takes as input microscopic images of two hairs and produces a classification decision as to whether or not the hairs came from the same person. Hair images were captured using a NEXTDimension™ video board in a NEXTDimension™ color turbo computer, connected to a video camera. Image processing was done on an SGI indigo workstation. Each image is segmented into a number of pieces appropriate for classification of different features. A variety of image processing techniques are used to enhance this information. Use of wavelet analysis and the Haralick texture algorithm to pre-process data has allowed us to compress large amounts of data into smaller, yet representative data. Neural networks are then used for feature classification. Finally, statistical tests determine the degree of match between the resulting collection of hair feature vectors. An important issue in automation of any task in criminal investigations is the reliability and understandability of the resulting system. To address this concern, we have developed methods to facilitate explanation of neural network’s behavior using a decision tree. The system was able to achieve a performance of 83% hair match accuracy, using 5 of the 21 morphological characteristics used by experts. This shows promise for the usefulness of a fuller scale system. While an automated system would not replace the expert, it would make the task easier by providing a means for pre-processing the large amount of data with which the expert must contend. [Copyright &y& Elsevier]

This paper presents a probabilistic algorithm for automatically extracting the stride interval time series from long, highly variable and noisy two-state timing signals. Long interstride temporal records are of particular interest in nonlinear dynamical analysis of gait. The proposed method consists of probabilistic estimation and extraction followed by post-extraction filtering. With noisy timing signals from 10 children with Spastic Diplegia, no statistical differences in the numbers of extracted strides (p=0.94), the mean stride intervals (p=0.55) and the scaling exponents (p=0.94) (a measure of temporal heterogeneity) were found between series extracted by hand and by the probabilistic algorithm. The method is robust to noise and violations of normality. Results support the use of probabilistic extraction as an alternative to laborious manual extraction. [Copyright &y& Elsevier]

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice. [ABSTRACT FROM AUTHOR]

Due to low tissue contrast, irregular shape, and large location variance, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, a novel method is presented for interactive medical image segmentation with the following merits. (1) Its design is fundamentally different from previous pure patch-based and image-based segmentation methods. It is observed that during delineation, the physician repeatedly check the intensity from area inside-object to outside-object to determine the boundary, which indicates that comparison in an inside-out manner is extremely important. Thus, the method innovatively models the segmentation task as learning the representation of bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by the proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), the proposed method only asks the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) The method is utilized in a multi-level framework for better performance. It has been systematically evaluated in three different segmentation tasks, including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through the use of linguistic if-then statements. Systems built on top of fuzzy sets are of particular appealing to medical applications since they enable the tolerance of vague and imprecise concepts that are often embedded in medical entities such as symptom description and test results. They facilitate an approximate reasoning framework which mimics human reasoning and supports the linguistic delivery of medical expertise often expressed in statements such as 'weight low' or 'glucose level high' while describing symptoms. This paper proposes an approach by performing data-driven learning of accurate and interpretable fuzzy rule bases for clinical decision support. The approach starts with the generation of a crisp rule base through a decision tree learning mechanism, capable of capturing simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the framework of adaptive network-based fuzzy inference system (ANFIS), thereby further optimising the parameters of both rule antecedents and consequents. Experimental studies on popular medical data benchmarks demonstrate that the proposed work is able to learn compact rule bases involving simple rule antecedents, with statistically better or comparable performance to those achieved by state-of-the-art fuzzy classifiers. [ABSTRACT FROM AUTHOR]

Automated detection of dynamical change in EEG signals has been a long-standing problem in a wide range of clinic applications. It is essential to extract an effective and accurate EEG rhythm indicator that can reflect the dynamical behavior of a given EEG signal. Time-frequency analysis is a promising method to achieve this end, but existing methods still have limitations in real implementation making this kind of methods still progressive until the present day. In this paper, along the line of ongoing research on time-frequency methods, we present a new method based on graph-based modeling. By virtue of this method, an effective and accurate EEG rhythm indicator can be extracted to characterize the dynamical EEG time series. Together with the extracted EEG rhythm indicator, an automatic analysis of continuous monitoring of EEG signal, is developed by means of a null hypothesis testing to inspect whether an EEG change occurs or not during a monitoring period. The proposed framework is applied to both simulated data and real signals respectively to validate its effectiveness. Experimental results, together with theoretical interpretation and discussions, suggest its promising potentials in practice. [ABSTRACT FROM AUTHOR]

Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based models have been developed for missing data imputation in recent papers. This approach makes use of the low-rank tensor assumption for highly correlated data in a short-time interval. Nevertheless, when the time intervals are long, data correlation may not be high between consecutive time stamps so that such assumption is not valid. To address this problem, we propose to decompose matrices with missing data over time into their latent factors. Then, the locally linear constraint is imposed on the latent factors for temporal matrix completion. By using three publicly available medical datasets and two medical datasets collected from Prince of Wales Hospital in Hong Kong, experimental results show that the proposed algorithm achieves the best performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: The Clinical Practice Research Datalink (CPRD) GOLD is an extremely influential U.K. primary care dataset for epidemiological research having a number of published papers based on its data much bigger than any other U.K. primary care dataset. The Office for National Statistics (ONS) death data for England can be linked to GOLD at the patient level and are considered the gold standard on mortality. GOLD, which also holds death data, has been recently assessed against ONS linked dataset and the accuracy of its dates of death has been deemed sufficient for the majority of observational studies. However, there is a lack of guidance on how to manage the challenges existing when ONS mortality and GOLD datasets are linked, including linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancy.Objectives: Provide reconciling guidelines on how to make maximum and at the same time trustworthy use of mortality information coming from both GOLD and ONS linked datasets with the aim of improving the quality, reproducibility, transparency and comparison of clinical research.Method and Results: We have developed recommendations on how to manage mortality data coming from both GOLD and linked ONS, taking into account linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancies between these two datasets. We have also implemented these guidelines in an SQL algorithm for researchers to use.Conclusion: We have provided detailed guidelines on the reconciliation of mortality data between GOLD and ONS linked death datasets, taking into account both their strengths and limitations. The consistent application of these guidelines made practical by an SQL algorithm, has the potential to improve clinical research quality, reproducibility, transparency and comparison. [ABSTRACT FROM AUTHOR]

Universal Health Coverage (UHC) has become a key goal of health policy in many developing countries. However, implementing UHC poses tough policy choices about: what treatments to provide (the depth of coverage); to what proportion of the population (the breadth of coverage); at what price to patients (the height of coverage). This paper uses a theoretical mathematical programming model to derive analytically the optimal balance between the range of services provided and the proportion of the population covered under UHC, using the general principles of cost-effectiveness analysis. In contrast to most CEA, the model allows for variations in both the costs of provision and the social benefits of treatments, depending on the deprivation level of the population. We illustrate empirically the optimal trade-off between the size of the benefits package and the proportion of the population securing access to each treatment for a hypothetical East African country, based on WHO data on the costs and benefits of treatments at different coverage levels. We begin with a scenario allowing coverage levels to vary, then apply differential equity weights to the benefits of coverage, and finally illustrate a scenario where interventions are either provided at 95% coverage or not at all (as is usually done in health benefits package design) for comparison. The results present the optimal trade-off between the social benefits of pursuing full population coverage, at the expense of expanding the benefits package for 'easier to reach' populations. [ABSTRACT FROM AUTHOR]

The Great Recession in Europe sparked concerns that the crisis would lead to increased income related health inequalities (IRHI). Did this come to pass, and what role, if any, did government transfers play in the evolution of these inequalities? Motivated by these questions, this paper seeks to (i) study the evolution of IRHI during the crisis, and (ii) decompose these evolutions to examine the separate roles of government versus market transfers. Using panel data for 7 EU countries from 2004 to 2013, we find no evidence that IRHI persistently rose after 2008, even in countries most affected by the crisis. Our decomposition reveals that, while the health of the poorest did indeed worsen during the crisis, IRHI were prevented from increasing by the relative stickiness of old age pension benefits compared to the market incomes of younger groups. Austerity measures weakened the IRHI reducing effect of government transfers. [ABSTRACT FROM AUTHOR]

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU. [ABSTRACT FROM AUTHOR]

The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery. For the fenestration, we propose an image-based state recognition method that consists a U-Net derived network, grayscale redistribution and dynamic receptive field assisting in controlling the grinding process to prevent the grinding-bit from crossing the inner edge of the lamina to damage the spinal nerves. For the internal fixation, we propose an audio and force-based state recognition method that consists signal features extraction methods, LSTM-based prediction and information fusion assisting in monitoring the drilling process to prevent the drilling-bit from crossing the outer edge of the vertebral pedicle to damage the spinal nerves. Several experiments are conducted to show the reliability of the proposed system in robot-assisted surgery. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Several statistical methods have been developed for analyzing multireader, multicase (MRMC) receiver operating characteristic (ROC) studies. The objective of this article is to increase awareness of these methods and determine if their results are concordant for published datasets. Materials and Methods: Data from three previously published studies were reanalyzed using five MRMC methods. For each method the 95% confidence intervals (CIs) for the mean of the readers' ROC areas for each diagnostic test, the P value for the comparison of the diagnostic tests' mean accuracies, and the 95% CIs for the mean difference in ROC areas of the diagnostic tests were reported. Results: Important differences in P values and CIs were seen when using parametric versus nonparametric estimates of accuracy, and there were the expected differences for random-reader versus fixed-reader models. Controlling for these differences, the Dorfman-Berbaum-Metz (DBM), Obuchowski-Rockette, Beiden-Wagner-Campbell, and Song's multivariate Wilcoxon-Mann-Whitney (WMW) methods gave almost identical results for the fixed-reader model. For the random-reader model, the DBM, Obuchowski-Rockette, and Beiden-Wagner-Campbell methods yielded approximately the same inferences, but the CIs for the Beiden-Wagner-Campbell method tend to be broader. Ishwaran's hierarchical ROC sometimes yielded significance not found with other methods. Song's modification of DBM's jack-knifing algorithm sometimes led to different conclusions than the original DBM algorithm. Conclusion: In choosing and applying MRMC methods, it is important to recognize: (1) the distinction between random-reader and fixed-reader models, the uncertainties accounted for by each, and thus the level of generalizeability expected from each; (2) assumptions made by the various MRMC methods; and (3) limitations of a five- or six-reader study when the reader variability is great. [ABSTRACT FROM AUTHOR]