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To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information. [ABSTRACT FROM AUTHOR]

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes. [ABSTRACT FROM AUTHOR]

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article's goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components' states in the neural network's output cells. Based on the condition of the object's constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement. [ABSTRACT FROM AUTHOR]

Increasing the efficiency and reliability of modern railway transport is accompanied by an increase in monitoring and diagnostic systems for the current state of electric drives. Modern railway transport contains a large number of induction motors to ensure the operation of the drives of various mechanisms. In the article, based on the operational statistics of engine failures and the proposed scheme for diagnosing them, studies were carried out and a model was developed for assessing the reliability of a transport system equipped with an on-board diagnostic system for the current state. When building the models, the Markov method was used, including the construction of graphs for the five most relevant states of the induction electric motor during operation. The results obtained are relevant for evaluating the effectiveness of using the built-in diagnostic system and scheduling routine maintenance, which will affect the efficiency of railway transport. Based on the process of the diagnosis of railway transport systems with induction motors, five operating states of the object studied were interpreted: the state of full operation, state "S0"; the state of incomplete serviceability, state "S1"; critical serviceability, state "S2"; the state of the pre-damage condition, state "S3"; the state of unserviceability (defect), state "S4". Subsequently, a five-state model of the operation process of railway transport systems with induction motors was developed. This model is also described by equations of state: Kolmogorov–Chapman equations. The reliability quantities determined form the basis for simulation reliability studies. The effect of the simulation study is the reliability quantities determined in the form of reliability functions and probabilities of the occurrences of the operating states of railway transport systems with induction motors; an important part of the reliability study of the system examined is to estimate the times of the occurrences in the object studied of the operating states in the future. [ABSTRACT FROM AUTHOR]

The article presents a continuation of research carried out to determine the effect of input parameters (changes in engine structure parameters) on selected output parameters (diagnostic measures), based on quickly changing exhaust gas temperature. A method of determining the simultaneous influence of two input factors (the structure parameter and the engine load) on one output factor was presented, as well as an evaluation of which of the analysed input factors has a stronger influence on the output parameter. The article presents the stages of the experimental research conducted and statistical inference based on the results. Three changing parameters for the structure were reviewed: the active cross-sectional area of the inlet air channel, the injector opening pressure and the compression ratio. Based on the quickly changing temperatures of the exhaust gases, three diagnostic measures were defined and subjected to statistical tests. The following data were averaged over one cycle for a 4-stroke engine operation: the intensity of changes, the specific enthalpy and the peak-to-peak value of the exhaust gas temperature. The results of the two-factor analysis are presented. Conclusions on the analysis are given and a criterion for the selection of a diagnostic measure, depending on the analysed parameter of the structural design of the diesel engine, is proposed. The previous part of the article presented the results of the first stage of the elimination study: the one-factor statistical analysis (randomised complete plan). This paper presents the results of the second stage of the studies: two-factor analysis (block randomised plan), where the significance of the effect of changing the values of the structural parameters on the diagnostic measures were analysed in the background of a variable engine load. The next (third) part will present the results of the calculations and analysis of the interaction coefficient of significance. [ABSTRACT FROM AUTHOR]

The article deals with simulation tests on the reliability of the equipment of the wind farm WF in the operation process. The improvement, modernization, and introduction of new solutions that change the reliability, as well as the quality and conditions of use and operation of wind farm equipment, require testing. Based on these tests, it is possible to continuously evaluate the reliability of the equipment of WF. The issue of reliability assessment of wind farm equipment, for which intelligent systems, diagnostic systems DIAG, and Wind Power Plant Expert System (WPPES) are used to modernize the operation process, can only be tested in a simulative way. The topic of testing the reliability of complex technical objects is constantly developing in the literature. In this paper, it is assumed that the operation of wind farm equipment is described and modeled based on Markov processes. The adoption of this assumption justified the use of the Kolmogorov–Chapman equations to describe the developed model. Based on this equation, an analytically developed model of the wind farm operation process was described. The simulation analysis determines the reliability of the wind farm in terms of the availability factor Kg(t). The simulation tests are performed in two phases using the computer program LabView. In the first stage, the reliability value in the form of the readiness factor Kg(t) as a function of changes in the mean repair time value ranging {from 0.3 to 1.0} was investigated. In the second stage, the reliability value of WF devices was examined as a function of changes in the value of the average time between successive failures, ranging from 1000 to 3000 (h)}. [ABSTRACT FROM AUTHOR]

Since 1948, Shannon theoretic methods for modeling information have found a wide range of applications in several areas where information plays a key role, which goes well beyond the original scopes for which they have been conceived, namely data compression and error correction over a noisy channel. Among other uses, these methods have been applied in the broad field of medical diagnostics since the 1970s, to quantify diagnostic information, to evaluate diagnostic test performance, but also to be used as technical tools in image processing and registration. This review illustrates the main contributions in assessing the accuracy of diagnostic tests and the agreement between raters, focusing on diagnostic test performance measurements and paired agreement evaluation. This work also presents a recent unified, coherent, and hopefully, final information-theoretical approach to deal with the flows of information involved among the patient, the diagnostic test performed to appraise the state of disease, and the raters who are checking the test results. The approach is assessed by considering two case studies: the first one is related to evaluating extra-prostatic cancers; the second concerns the quality of rapid tests for COVID-19 detection. [ABSTRACT FROM AUTHOR]

In this paper, attention was paid to the problem of low controllability of marine medium- and high-speed engines during operation, which significantly limits the parametric diagnosis. The measurement of quickly changing temperature of engine exhaust gas was proposed, the courses of which can be a source of diagnostic information. The F statistic of the Fisher-Snedecor distribution was chosen as a statistical tool. Laboratory tests were carried out on the bench of a Farymann Diesel engine. The tests consisted of introducing the real changes in the constructional structure of the considered functional systems of the engine. Three changed parameters for the structure were reviewed: the active cross-sectional area of the inlet air channel, injector opening pressure and compression ratio. Based on the recorded plots of the quick-changing temperatures of the exhaust gases, three diagnostic measures were defined and subjected to statistical tests. The following data were averaged over one cycle for a 4-stroke piston engine operation, (1) the peak-to-peak value of the exhaust gas temperature, (2) the specific enthalpy of the exhaust gas, and (3) the rate of increase and decrease in the values for the quick-changing exhaust gas temperature. In this paper will present results of the first stage of the elimination study: the one-factor statistical analysis (randomised complete plan). The next part will present the results of the second stage of studies: two-factor analysis (block randomised plan), where the significance of the effect of changing the values of the structure parameters on the diagnostic measures was analysed in the background of a variable engine load. [ABSTRACT FROM AUTHOR]

Previous studies have found that the labour market outcomes of Turkish minorities are slightly better in Germany than in the Netherlands. In this paper we test one of the explanations: differences in ethnic discrimination in hiring. We use a harmonised field experiment to test whether discrimination against job candidates of Turkish origin (age 23–25) varies across Germany and the Netherlands, while holding individual characteristics of job seekers constant. We find that, compared to majority candidates, job candidates of Turkish origin are on average eleven percentage points less likely to receive a positive call-back. Moreover, we find that discrimination against Turkish minorities is significantly higher in the Netherlands than in Germany. In Germany, job candidates of Turkish origin are five percentage points less likely to receive a call-back than equally qualified majority candidates, whereas in the Netherlands this ethnic gap is fifteen percentage points. However, the presented evidence does not support the often-mentioned argument that the amount of diagnostic information in application materials explains why discrimination against Turkish minorities is lower in Germany. Overall, adding diagnostic information has little effect on the relative employment chances of job applicants of Turkish origin, both in Germany and the Netherlands. [ABSTRACT FROM AUTHOR]

Image fusion in neuro diagnosis is intimidating due to its complexity. The heterogeneous natures of the original brain images make intermodal transmission difficult during fusion. Medical image fusion using complementary modalities results in loss of vital salient information. Poor fusion, colour deficiencies result due to similar processing for both the modalities. A dual technique is proposed using discrete fractional wavelet transform (FRWT) and non‐subsampled directional filter banks for better extraction of salient image elements for improved diagnosis. The sparsity character of the coefficients FRWT is controlled by optimising the parity operator using Grey Wolf optimisation algorithm. Four sets of neurological multimodal magnetic resonance imaging and single photon emission computed tomography (CT) brain images are used from benchmark database for validation. The objective evaluation has been conducted using five metrics. The main values obtained from objective metrics based on the proposed technique are 6.3213 for Shannon entropy, mutual information is computed to be 2.7582, fusion factor is 1.9095, standard deviation is 0.1310, and edge strength is 0.76122 indicating improved diagnostic information and superior image quality. Subjective evaluation by a medico validates the findings with finer visual output and enhanced contrast in comparison with recent and state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

We presented 3 studies on the relation between metacognitive self, that is self- awareness of biases, and the drive to posses information that is diagnostic about the outcome. First study (N=184) showed that high MCS participants sought self- diagnostic information significantly more often then their low MCS counter partners. Second study is devoted to the development of the new measure SDMS (N = 555), that is the motive to look for self -- diagnostic information. The last study (N = 90) presented that high MCS participants in contrary to low MCS ones, posses higher drive for looking self- diagnostic information when feedback about the outcome is negative. We discuss the results in line with Taylor & Brown (1988) reasoning on self- regulatory functions of biases. [ABSTRACT FROM AUTHOR]

• AIC is introduced to pick the segmentation point where the spectral distribution characteristics change suddenly. • A dichotomy-based AIC (d -AIC) picker is proposed to divide the amplitude spectrum hierarchically. • Not only the out-band noise but also the in-band interference has been eliminated in the proposed two-stage strategy. Fault-excited repetitive transients are apt to be submerged in unavoidable noise. Till now, most vibration-based diagnostic methods just reject the noise outside the optimal frequency band without suppressing the noise in the candidate band, which could not extract the diagnostic information with a high signal-to-noise ratio (SNR). Motivated by this, a new two-stage strategy is proposed to reduce the out-band strong noise and further eliminate the in-band interference in different stages. In the first stage, a dichotomy-based AIC (d -AIC) picker is designed to determine the spectral segmentation points. The obtained frequency boundaries are utilized to design the filter banks so that the signal components outside the passband of each sub-band signal are completely rejected. Subsequently, in the second stage, the filter characteristics are finely tuned to further enrich the fault-related signatures and suppress the in-band interferences of these sub-band signals. Based on the distribution characteristics embedded in the whole spectral series, the designed d -AIC shows superiority in the spectrum-driven frequency division. Moreover, the proposed two-stage strategy can provide health information with a high SNR about the monitoring bearing system. The simulated and experimental scenarios give credible evidence about the advantages of the proposed strategy, which means the proposed scheme is powerful to realize a more comprehensive condition monitoring in practical application. [ABSTRACT FROM AUTHOR]

This article describes a procedure for the probabilistic estimation of the strength indices of steel wire ropes on the basis of the data of magnetic non-destructive testing. The proposed method for the determination of probabilistic characteristics undetection an existing fault of a heterogeneous wire rope structure and the false detection of a fault. The obtained probabilistic estimates of errors of nondestructive testing have been applied for calculations of high-risk and conservative scenarios of the variation of the strength and remaining lifetime of wire rope during its non-fault operation lifetime. [ABSTRACT FROM AUTHOR]

The present paper covers the issue of training of an artificial neural network in an intelligent diagnostic system whose purpose is to evaluate repairable technical objects. The structure of the diagnostic system was characterized, and the measurement and diagnostic subsystems were described. An artificial neural network is an important element in an intelligent diagnostic subsystem. The structure, the algorithm, the organization of a neural network and the basic relations that describe its work were presented. The information presented in the form of the vectors of diagnostic signals, and their standard vectors constitute the primary information base used in 'DIAG' computer program. Training of an artificial neural network is an important aspect that is presented in the paper. The issue concerning these problems is not presented in the literature. Training of a network was presented on the grounds of teaching vectors, which are determined in a diagnostic system in the process of a simulation of a specific state in the object examined. An example of training of a network was presented in a diagnostic system which evaluates a control system of the operation of a car engine. Appropriate connections were presented for the purpose of a qualitative assessment of the training process of a neural network. [ABSTRACT FROM AUTHOR]

Higher visual areas in the occipitotemporal cortex contain discrete regions for face processing, but it remains unclear if V1 is modulated by top-down influences during face discrimination, and if this is widespread throughout V1 or localized to retinotopic regions processing task-relevant facial features. Employing functional magnetic resonance imaging ( fMRI), we mapped the cortical representation of two feature locations that modulate higher visual areas during categorical judgements - the eyes and mouth. Subjects were presented with happy and fearful faces, and we measured the fMRI signal of V1 regions processing the eyes and mouth whilst subjects engaged in gender and expression categorization tasks. In a univariate analysis, we used a region-of-interest-based general linear model approach to reveal changes in activation within these regions as a function of task. We then trained a linear pattern classifier to classify facial expression or gender on the basis of V1 data from 'eye' and 'mouth' regions, and from the remaining non-diagnostic V1 region. Using multivariate techniques, we show that V1 activity discriminates face categories both in local 'diagnostic' and widespread 'non-diagnostic' cortical subregions. This indicates that V1 might receive the processed outcome of complex facial feature analysis from other cortical (i.e. fusiform face area, occipital face area) or subcortical areas (amygdala). [ABSTRACT FROM AUTHOR]

The issues of the examination and determination of the ambiguity of the identification of the states of a technical object in a diagnostic system with an artificial neural network are presented in this article. It is an important problem in the operation of each inference (decision) system, in which various types of decisions are worked out, particularly including ones for diagnostic systems. For this purpose, a diagnostic system including its elements, in which an artificial neural network is used, is characterized and described. The structure together with the algorithm of a diagnostic neural network is presented. A diagram was drawn up, and the circulation of information in the diagnostic system was described in the perspective of errors brought into decision information by the individual elements of this system. A formula for the general error during working out of decisions in the system is put forward. It was also indicated that a number of factors including interferences and influence of the environment, errors during the measurement of the values of the properties of diagnostic signals, errors at the determination of the ranges of possible (permissible and limiting) changes of the properties of diagnostic signals defined in the inference rules and the accuracy of the drawing up of diagnostic inference rules have all a direct impact on the ambiguity level of the identification of the states of a technical object. In the present article, it is also the possibilities that are put forward for the determination and optimization of the ambiguity of the identification of the states of a technical object. For this purpose, the function that determines the quality of the identification and non-identification of the object's state was defined in this article. A practical assessment method of the ambiguity level of the identification of the states of a technical object in the examined diagnostic system is additionally presented in an example with the use of a radar system. [ABSTRACT FROM AUTHOR]

This paper presents a method to creation of a servicing expert system including an artificial neural network. The theoretical basis was presented with the model of an operation process of objects in the form of the following models: mathematical (analytical), graphical and descriptional. For the tests, a model was developed of an organization of a servicing technical system of those technical objects which require short shutdown times (aircrafts, radiolocation systems, etc.). The mathematical basis was presented for the execution of the task of servicing of a technical object. The idea of the servicing of the object was described as a transformation of the properties of the operational function of the object from the space of the current servicing to the form of the space of the features of the nominal (model) operation of the object. The results were presented of the radar system. [ABSTRACT FROM AUTHOR]

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object. [ABSTRACT FROM AUTHOR]

This paper presents a method to investigate the effectiveness of an operation process with a servicing expert system including an artificial neural network. A method of simulation testing with the use of computer technology was described. The theoretical basis was presented of the modelling of an operation process of objects in the form of the following models: mathematical (analytical), graphical and descriptional. For the tests, a model was developed of an organization of a servicing system of those technical objects which require short shutdown times (aircrafts, radiolocation systems, etc.). The requirements were presented and described for simulation tests, which is the development of a test plan, preparation of data to describe the performance of an object, as well as development of models for an operation process of a technical object, which express the investigated aspects of this process. The results of the simulation tests of a repairable technical object were presented. [ABSTRACT FROM AUTHOR]

The nature of a sign, its functions, distinctive features of a sign situation in clinical diagnostics, and also various aspects of transpersonal communications are investigated. Objective indications such as signs, symptoms, and their verbal denotations are considered that are necessary to translate diagnostic information from the level of signals to the level of a message by extraction their meaning during their understanding. [ABSTRACT FROM AUTHOR]

The usefulness of electronic databases to medical profession is expanding rapidly. As the utilization of e-databases spread, accessible to more medical and biomedical resources is facilitated. Fifty medical laboratory scientists currently practising in the University Teaching Hospital were used as respondents. The aim of the study was to determine usability of e-resources, impact on the practice of their profession, areas of training needs for effective utilization. Questionnaire was used to elicit information from the respondents. The study revealed that a good use was made of e-resources available, although some hindrances were highlighted. Early completion of computerization process of the library is highly essential, intensive campus awareness campaign of e-resources and soft loan for the staff to purchase personal computers are ways of encouraging staff to use-resources. [ABSTRACT FROM AUTHOR]

Abstract: There is behavioral evidence that different visual categorization tasks on various types of stimuli (e.g., faces) are sensitive to distinct visual characteristics of the same image, for example, spatial frequencies. However, it has been more difficult to address the question of how early in the processing stream this sensitivity to the information relevant to the categorization task emerges. The current study uses scalp event-related potentials recorded in humans to examine how and when information diagnostic to a particular task is processed during that task versus during a task for which it is not diagnostic. Subjects were shown diagnostic and anti-diagnostic face images for both expression and gender decisions (created using Gosselin and Schyns’ Bubbles technique), and asked to perform both tasks on all stimuli. Behaviorally, there was a larger advantage of diagnostic over anti-diagnostic facial images when images designed to be diagnostic for a particular task were shown when performing that task, as compared to performing the other task. Most importantly, this interaction was seen in the amplitude of the occipito-temporal N170, a visual component reflecting a perceptual stage of processing associated with the categorization of faces. When participants performed the gender categorization task, the N170 amplitude was larger when they were presented with gender diagnostic images than with expression-diagnostic images, relative to their respective non-diagnostic stimuli. However, categorizing faces according to their facial expression was not significantly associated with a larger N170 when subjects categorized expression diagnostic cues relative to gender-diagnostic cues. These results show that the influence of higher-level task-oriented processing may take place at the level of visual categorization stages for faces, at least for processes relying on shared diagnostic features with facial identity judgments, such as gender cues. [Copyright &y& Elsevier]

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object's elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement. [ABSTRACT FROM AUTHOR]

[18F]-FDG-PET/CT ([18F]-fluoro-deoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)) is increasingly used as a diagnostic tool in suspected infectious or inflammatory conditions. Studies on the value of FDG-PET/CT in children are scarce. This study assesses the role of FDG-PET/CT in suspected infection or inflammation in children. In this multicenter cohort study, 64 scans in 59 children with suspected infection or inflammation were selected from 452 pediatric FDG-PET/CT scans, performed in five hospitals between January 2016 and August 2017. Main outcomes were diagnostic information provided by FDG-PET/CT for diagnostic scans and impact on clinical management for follow-up scans. Of these 64 scans, 50 were performed for primary diagnosis and 14 to monitor disease activity. Of the positive diagnostic scans, 23/27 (85%) contributed to establishing a diagnosis. Of the negative diagnostic scans, 8/21 (38%) contributed to the final diagnosis by narrowing the differential or by providing information on the disease manifestation. In all follow-up scans, FDG-PET/CT results guided management decisions. CRP was significantly higher in positive scans than in negative scans (p = 0.004). In 6% of diagnostic scans, relevant incidental findings were identified. In conclusion, FDG-PET/CT performed in children with suspected infection or inflammation resulted in information that contributed to the final diagnosis or helped to guide management decisions in the majority of cases. Prospective studies assessing the impact of FDG-PET/CT results on diagnosis and patient management using a structured diagnostic protocol are feasible and necessary. [ABSTRACT FROM AUTHOR]

This paper develops a test of goodness-of-fit for elliptical distributions. The test is invariant to affine-linear transformations and has a convenient expression that can be broken into components containing diagnostic information to be used to identify possible departures when the test rejects. The test is developed for the bivariate Laplace, logistic and Pearson type II distributions, as well as the multivariate normal for which the R package ECGofTestDx is available on the CRAN. A simulation shows the usefulness of the diagnostic tools. [ABSTRACT FROM AUTHOR]

Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts. [ABSTRACT FROM AUTHOR]