Brain activity and medical diagnosis: an EEG study

Abstract Background Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis Results The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making. Conclusions PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

From complexity to category: Responding to diagnostic uncertainties of autistic spectrum disorders

Objective: Recent data from Education Queensland has identified rising numbers of children receiving diagnoses of autistic spectrum disorder (ASD). Faced with funding diagnostic pressures, in clinical situations that are complex and inherently uncertain, it is possible that specialists err on the side of a positive diagnosis. This study examines the extent to which possible overinclusion of ASD diagnosis may exist in the presence of uncertainty and factors potentially related to this practice in Queensland. Methods: Using anonymous self-report, all Queensland child psychiatrists and paediatricians who see paediatric patients with development/behavioural problems were surveyed and asked whether they had ever specified an ASD diagnosis in the presence of diagnostic uncertainty. Using logistic regression, elicited responses to the diagnostic uncertainty questions were related to other clinical- and practice-related characteristics. Results: Overall, 58% of surveyed psychiatrists and paediatricians indicated that, in the face of diagnostic uncertainty, they had erred on the side of providing an ASD diagnosis for educational ascertainment and 36% of clinicians had provided an autism diagnosis for Carer's Allowance when Centrelink diagnostic specifications had not been met. Conclusion: In the absence of definitive biological markers, ASD remains a behavioural diagnosis that is often complex and uncertain. In response to systems that demand a categorical diagnostic response, specialists are providing ASD diagnoses, even when uncertain. The motivation for this practice appears to be a clinical risk/benefit analysis of what will achieve the best outcomes for children. It is likely that these practices will continue unless systems change eligibility to funding based on functional impairment rather than medical diagnostic categories.

ROLE OF ENTERPRISE ARCHITECTURE IN HEALTHCARE ORGANIZATIONS AND KNOWLEDGE-BASED MEDICAL DIAGNOSIS SYSTEM

Enterprise architecture (EA) is a tool that aligns organization’s business-process with application and information technology (IT) through EAmodels. This EA model allows the organization to cut off unnecessary IT expenses and determines the future and current IT requirements and boosts organizational performance. Enterprise architecture may be employed in every firm where the firm or organization requires configurations between information technology and business functions. This research investigates the role of enterprise architecture in healthcare organizations and suggests the suitable EA framework for knowledge-based medical diagnostic system for EA modeling by comparing the two most widely used EA frameworks. The results of the comparison identified that the proposed EA has a better framework for knowledge-based medical diagnostic system.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Estimation of the noisy component of anatomical backgrounds.

The knowledge of the relationship that links radiation dose and image quality is a prerequisite to any optimization of medical diagnostic radiology. Image quality depends, on the one hand, on the physical parameters such as contrast, resolution, and noise, and on the other hand, on characteristics of the observer that assesses the image. While the role of contrast and resolution is precisely defined and recognized, the influence of image noise is not yet fully understood. Its measurement is often based on imaging uniform test objects, even though real images contain anatomical backgrounds whose statistical nature is much different from test objects used to assess system noise. The goal of this study was to demonstrate the importance of variations in background anatomy by quantifying its effect on a series of detection tasks. Several types of mammographic backgrounds and signals were examined by psychophysical experiments in a two-alternative forced-choice detection task. According to hypotheses concerning the strategy used by the human observers, their signal to noise ratio was determined. This variable was also computed for a mathematical model based on the statistical decision theory. By comparing theoretical model and experimental results, the way that anatomical structure is perceived has been analyzed. Experiments showed that the observer's behavior was highly dependent upon both system noise and the anatomical background. The anatomy partly acts as a signal recognizable as such and partly as a pure noise that disturbs the detection process. This dual nature of the anatomy is quantified. It is shown that its effect varies according to its amplitude and the profile of the object being detected. The importance of the noisy part of the anatomy is, in some situations, much greater than the system noise. Hence, reducing the system noise by increasing the dose will not improve task performance. This observation indicates that the tradeoff between dose and image quality might be optimized by accepting a higher system noise. This could lead to a better resolution, more contrast, or less dose.

Validação da termografia no diagnóstico de lesões por esforços repetitivos/distúrbios osteomusculares relacionados ao trabalho

As Lesões por Esforços Repetitivos e Doenças Osteomusculares Relacionadas ao Trabalho estão caracterizadas por queixas de grande incapacidade funcional, causadas pelo uso dos membros superiores em tarefas que envolvam movimentos repetitivos. Esses distúrbios também podem ocorrer nas atividades de lazer ou no cotidiano das pessoas, especialmente quando realizadas por períodos longos, sem pausas adequadas para recuperação muscular, ou ainda, sem devido acompanhamento médico. A avaliação de doenças ocupacionais é eminentemente clínica. São raros os casos em que os exames complementares apóiam o diagnóstico médico. Diante disso, o objetivo desta pesquisa foi verificar a eficácia da técnica de Termografia no diagnóstico de LER/DORT. A amostra (n=51) foi dividida em dois grupos. Um grupo experimental (n=41) composto por 26 Taquigrafas da Assembléia Legislativa do Rio Grande do Sul e 15 digitadores da Companhia Estadual de Energia Elétrica do Rio Grande do Sul e um grupo controle (n=10) composto por participantes com outras profissões. Todos eles estavam de acordo com o que prescreve a resolução 196/96 do Conselho Nacional de Saúde. Os resultados do teste Q de Cochran mostraram diferenças estatisticamente significativas entre os 3 observadores da Termografia nas regiões dos punhos, cotovelos e ombros para o teste. Os coeficientes de concordância Kappa de cada observador no teste e reteste foram elevados. O coeficiente de concordância Kappa entre os observadores no teste mostraram que os 3 observadores estavam seguros para diagnosticar punho direito e cotovelos. Tais resultados mostram que a Termografia foi mais sensível e específica que a Ecografia e o Exame Clínico nos punhos direito e esquerdo. Assim, podemos inferir que os dados obtidos através das imagens termográficas permitiram uma melhor visualização das áreas traumatizadas nos componentes da amostra.

Estudo comparativo de métodos ultra-sonográficos de avaliação da idade gestacional em cadelas

Pós-graduação em Medicina Veterinária - FMVZ

Estudo prospectivo do diagnóstico por imagem em cães com ruptura do ligamento cruzado cranial. -

Pós-graduação em Medicina Veterinária - FMVZ

Proposta de biomodelagem virtual utilizando softwares livres

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Perfil de pacientes portadores de gastrostomia e o papel dos cuidadores no domicílio

Pós-graduação em Enfermagem (mestrado profissional) - FMB

Design and Characterization of New Luminescent Sensors and Labels

The aim of this Ph.D. project has been the design and characterization of new and more efficient luminescent tools, in particular sensors and labels, for analytical chemistry, medical diagnostics and imaging. Actually both the increasing temporal and spatial resolutions that are demanded by those branches, coupled to a sensitivity that is required to reach the single molecule resolution, can be provided by the wide range of techniques based on luminescence spectroscopy. As far as the development of new chemical sensors is concerned, as chemists we were interested in the preparation of new, efficient, sensing materials. In this context, we kept developing new molecular chemosensors, by exploiting the supramolecular approach, for different classes of analytes. In particular we studied a family of luminescent tetrapodal-hosts based on aminopyridinium units with pyrenyl groups for the detection of anions. These systems exhibited noticeable changes in the photophysical properties, depending on the nature of the anion; in particular, addition of chloride resulted in a conformational change, giving an initial increase in excimeric emission. A good selectivity for dicarboxylic acid was also found. In the search for higher sensitivities, we moved our attention also to systems able to perform amplification effects. In this context we described the metal ion binding properties of three photoactive poly-(arylene ethynylene) co-polymers with different complexing units and we highlighted, for one of them, a ten-fold amplification of the response in case of addition of Zn2+, Cu2+ and Hg2+ ions. In addition, we were able to demonstrate the formation of complexes with Yb3+ an Er3+ and an efficient sensitization of their typical metal centered NIR emission upon excitation of the polymer structure, this feature being of particular interest for their possible applications in optical imaging and in optical amplification for telecommunication purposes. An amplification effect was also observed during this research in silica nanoparticles derivatized with a suitable zinc probe. In this case we were able to prove, for the first time, that nanoparticles can work as “off-on” chemosensors with signal amplification. Fluorescent silica nanoparticles can be thus seen as innovative multicomponent systems in which the organization of photophysically active units gives rise to fruitful collective effects. These precious effects can be exploited for biological imaging, medical diagnostic and therapeutics, as evidenced also by some results reported in this thesis. In particular, the observed amplification effect has been obtained thanks to a suitable organization of molecular probe units onto the surface of the nanoparticles. In the effort of reaching a deeper inside in the mechanisms which lead to the final amplification effects, we also attempted to find a correlation between the synthetic route and the final organization of the active molecules in the silica network, and thus with those mutual interactions between one another which result in the emerging, collective behavior, responsible for the desired signal amplification. In this context, we firstly investigated the process of formation of silica nanoparticles doped with pyrene derivative and we showed that the dyes are not uniformly dispersed inside the silica matrix; thus, core-shell structures can be formed spontaneously in a one step synthesis. Moreover, as far as the design of new labels is concerned, we reported a new synthetic approach to obtain a class of robust, biocompatible silica core-shell nanoparticles able to show a long-term stability. Taking advantage of this new approach we also showed the synthesis and photophysical properties of core-shell NIR absorbing and emitting materials that proved to be very valuable for in-vivo imaging. In general, the dye doped silica nanoparticles prepared in the framework of this project can conjugate unique properties, such as a very high brightness, due to the possibility to include many fluorophores per nanoparticle, high stability, because of the shielding effect of the silica matrix, and, to date, no toxicity, with a simple and low-cost preparation. All these features make these nanostructures suitable to reach the low detection limits that are nowadays required for effective clinical and environmental applications, fulfilling in this way the initial expectations of this research project.

Analysis and modeling techniques for ultrasonic tissue characterization

This thesis introduces new processing techniques for computer-aided interpretation of ultrasound images with the purpose of supporting medical diagnostic. In terms of practical application, the goal of this work is the improvement of current prostate biopsy protocols by providing physicians with a visual map overlaid over ultrasound images marking regions potentially affected by disease. As far as analysis techniques are concerned, the main contributions of this work to the state-of-the-art is the introduction of deconvolution as a pre-processing step in the standard ultrasonic tissue characterization procedure to improve the diagnostic significance of ultrasonic features. This thesis also includes some innovations in ultrasound modeling, in particular the employment of a continuous-time autoregressive moving-average (CARMA) model for ultrasound signals, a new maximum-likelihood CARMA estimator based on exponential splines and the definition of CARMA parameters as new ultrasonic features able to capture scatterers concentration. Finally, concerning the clinical usefulness of the developed techniques, the main contribution of this research is showing, through a study based on medical ground truth, that a reduction in the number of sampled cores in standard prostate biopsy is possible, preserving the same diagnostic power of the current clinical protocol.

Diffusion of laser polarized gases in MRI

The use of Magnetic Resonance Imaging (MRI) as a diagnostic tool is increasingly employing functional contrast agents to study or contrast entire mechanisms. Contrast agents in MRI can be classified in two categories. One type of contrast agents alters the NMR signal of the protons in its surrounding, e.g. lowers the T1 relaxation time. The other type enhances the Nuclear Magnetic Resonance (NMR) signal of specific nuclei. For hyperpolarized gases the NMR signal is improved up to several orders of magnitude. However, gases have a high diffusivity which strongly influences the NMR signal strength, hence the resolution and appearance of the images. The most interesting question in spatially resolved experiments is of course the achievable resolution and contrast by controlling the diffusivity of the gas. The influence of such diffusive processes scales with the diffusion coefficient, the strength of the magnetic field gradients and the timings used in the experiment. Diffusion may not only limit the MRI resolution, but also distort the line shape of MR images for samples, which contain boundaries or diffusion barriers within the sampled space. In addition, due to the large polarization in gaseous 3He and 129Xe, spin diffusion (different from particle diffusion) could play a role in MRI experiments. It is demonstrated that for low temperatures some corrections to the NMR measured diffusion coefficient have to be done, which depend on quantum exchange effects for indistinguishable particles. Physically, if these effects can not change the spin current, they can do it indirectly by modifying the velocity distribution of the different spin states separately, so that the subsequent collisions between atoms and therefore the diffusion coefficient can eventually be affected. A detailed study of the hyperpolarized gas diffusion coefficient is presented, demonstrating the absence of spin diffusion (different from particle diffusion) influence in MRI at clinical conditions. A novel procedure is proposed to control the diffusion coefficient of gases in MRI by admixture of inert buffer gases. The experimental measured diffusion agrees with theoretical simulations. Therefore, the molecular mass and concentration enter as additional parameters into the equations that describe structural contrast. This allows for setting a structural threshold up to which structures contribute to the image. For MRI of the lung this allows for images of very small structural elements (alveoli) only, or in the other extreme, all airways can be displayed with minimal signal loss due to diffusion.

X-ray phase contrast imaging of metallized and non-metallized biological samples

Imaging of biological samples has been performed with a variety of techniques for example electromagnetic waves, electrons, neutrons, ultrasound and X-rays. Also conventional X-ray imaging represents the basis of medical diagnostic imaging, it remains of limited use in this application because it is based solely on the differential absorption of X-rays by tissues. Coherent and bright photon beams, such as those produced by third-generation synchrotron X-ray sources, provide further information on subtle X-ray phase changes at matter interfaces. This complements conventional X-ray absorption by edge enhancement phenomena. Thus, phase contrast imaging has the potential to improve the detection of structures on images by detecting those structures that are invisible with X-ray absorption imaging. Images of a weakly absorbing nylon fibre were recorded in in-line holography geometry using a high resolution low-noise CCD camera at the ESRF in Grenoble. The method was also applied to improve image contrast for images of biological tissues. This paper presents phase contrast microradiographs of vascular tree casts and images of a housefly. These reveal very fine structures, that remain invisible with conventional absorption contrast only.

Optimización de aperturas mediante técnicas de algoritmos evolutivos

Los sistemas de imagen por ultrasonidos son hoy una herramienta indispensable en aplicaciones de diagnóstico en medicina y son cada vez más utilizados en aplicaciones industriales en el área de ensayos no destructivos. El array es el elemento primario de estos sistemas y su diseño determina las características de los haces que se pueden construir (forma y tamaño del lóbulo principal, de los lóbulos secundarios y de rejilla, etc.), condicionando la calidad de las imágenes que pueden conseguirse. En arrays regulares la distancia máxima entre elementos se establece en media longitud de onda para evitar la formación de artefactos. Al mismo tiempo, la resolución en la imagen de los objetos presentes en la escena aumenta con el tamaño total de la apertura, por lo que una pequeña mejora en la calidad de la imagen se traduce en un aumento significativo del número de elementos del transductor. Esto tiene, entre otras, las siguientes consecuencias: Problemas de fabricación de los arrays por la gran densidad de conexiones (téngase en cuenta que en aplicaciones típicas de imagen médica, el valor de la longitud de onda es de décimas de milímetro) Baja relación señal/ruido y, en consecuencia, bajo rango dinámico de las señales por el reducido tamaño de los elementos. Complejidad de los equipos que deben manejar un elevado número de canales independientes. Por ejemplo, se necesitarían 10.000 elementos separados λ 2 para una apertura cuadrada de 50 λ. Una forma sencilla para resolver estos problemas existen alternativas que reducen el número de elementos activos de un array pleno, sacrificando hasta cierto punto la calidad de imagen, la energía emitida, el rango dinámico, el contraste, etc. Nosotros planteamos una estrategia diferente, y es desarrollar una metodología de optimización capaz de hallar de forma sistemática configuraciones de arrays de ultrasonido adaptados a aplicaciones específicas. Para realizar dicha labor proponemos el uso de los algoritmos evolutivos para buscar y seleccionar en el espacio de configuraciones de arrays aquellas que mejor se adaptan a los requisitos fijados por cada aplicación. En la memoria se trata el problema de la codificación de las configuraciones de arrays para que puedan ser utilizados como individuos de la población sobre la que van a actuar los algoritmos evolutivos. También se aborda la definición de funciones de idoneidad que permitan realizar comparaciones entre dichas configuraciones de acuerdo con los requisitos y restricciones de cada problema de diseño. Finalmente, se propone emplear el algoritmo multiobjetivo NSGA II como herramienta primaria de optimización y, a continuación, utilizar algoritmos mono-objetivo tipo Simulated Annealing para seleccionar y retinar las soluciones proporcionadas por el NSGA II. Muchas de las funciones de idoneidad que definen las características deseadas del array a diseñar se calculan partir de uno o más patrones de radiación generados por cada solución candidata. La obtención de estos patrones con los métodos habituales de simulación de campo acústico en banda ancha requiere tiempos de cálculo muy grandes que pueden hacer inviable el proceso de optimización con algoritmos evolutivos en la práctica. Como solución, se propone un método de cálculo en banda estrecha que reduce en, al menos, un orden de magnitud el tiempo de cálculo necesario Finalmente se presentan una serie de ejemplos, con arrays lineales y bidimensionales, para validar la metodología de diseño propuesta comparando experimentalmente las características reales de los diseños construidos con las predicciones del método de optimización. ABSTRACT Currently, the ultrasound imaging system is one of the powerful tools in medical diagnostic and non-destructive testing for industrial applications. Ultrasonic arrays design determines the beam characteristics (main and secondary lobes, beam pattern, etc...) which assist to enhance the image resolution. The maximum distance between the elements of the array should be the half of the wavelength to avoid the formation of grating lobes. At the same time, the image resolution of the target in the region of interest increases with the aperture size. Consequently, the larger number of elements in arrays assures the better image quality but this improvement contains the following drawbacks: Difficulties in the arrays manufacturing due to the large connection density. Low noise to signal ratio. Complexity of the ultrasonic system to handle large number of channels. The easiest way to resolve these issues is to reduce the number of active elements in full arrays, but on the other hand the image quality, dynamic range, contrast, etc, are compromised by this solutions In this thesis, an optimization methodology able to find ultrasound array configurations adapted for specific applications is presented. The evolutionary algorithms are used to obtain the ideal arrays among the existing configurations. This work addressed problems such as: the codification of ultrasound arrays to be interpreted as individuals in the evolutionary algorithm population and the fitness function and constraints, which will assess the behaviour of individuals. Therefore, it is proposed to use the multi-objective algorithm NSGA-II as a primary optimization tool, and then use the mono-objective Simulated Annealing algorithm to select and refine the solutions provided by the NSGA I I . The acoustic field is calculated many times for each individual and in every generation for every fitness functions. An acoustic narrow band field simulator, where the number of operations is reduced, this ensures a quick calculation of the acoustic field to reduce the expensive computing time required by these functions we have employed. Finally a set of examples are presented in order to validate our proposed design methodology, using linear and bidimensional arrays where the actual characteristics of the design are compared with the predictions of the optimization methodology.