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.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Comparing the content of instruments assessing environmental factors using the International Classification of Functioning, Disability and Health

Purpose: To describe and compare the content of instruments that assess environmental factors using the International Classification of Functioning, Disability and Health (ICF). Methods: A systematic search of PubMed, CINAHL and PEDro databases was conducted using a pre-determined search strategy. The identified instruments were screened independently by two investigators, and meaningful concepts were linked to the most precise ICF category according to published linking rules. Results: Six instruments were included, containing 526 meaningful concepts. Instruments had between 20% and 98% of items linked to categories in Chapter 1. The highest percentage of items from one instrument linked to categories in Chapters 2–5 varied between 9% and 50%. The presence or absence of environmental factors in a specific context is assessed in 3 instruments, while the other 3 assess the intensity of the impact of environmental factors. Discussion: Instruments differ in their content, type of assessment, and have several items linked to the same ICF category. Most instruments primarily assess products and technology (Chapter 1), highlighting the need to deepen the discussion on the theory that supports the measurement of environmental factors. This discussion should be thorough and lead to the development of methodologies and new tools that capture the underlying concepts of the ICF.

Comorbidity adjustment index for the international classification of diseases, 10

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

Factores psicossociais preditivos de ajustamento à vida de pessoas com doenças crónicas

Doenças crónicas são de longa duração, de progressão lenta e induzem alterações na vida das pessoas, que são confrontadas com um conjunto de fatores que exercem um impacto negativo na sua qualidade de vida (QdV). A QdV é um conceito envolvendo componentes essenciais da qualidade humana: físicas, psicológicas, sociais, culturais e espirituais. Após o diagnóstico e com a doença estabilizada, os doentes procuram novas formas de lidar com esta. Este estudo teve como objetivo identificar fatores psicossociais preditivos (otimismo, afeto positivo e negativo, adesão aos tratamentos, suporte social e espiritualidade) da QdV (bem-estar geral, saúde física, saúde mental) e bem-estar subjetivo (BES) em pessoas com doenças crónicas. Amostra constituída por 774 indivíduos [30% diabetes, 27,1% cancro, 17,2% diabetes, 12% epilepsia, 11,5% esclerose múltipla e 2,2% miastenia, 70,5% do sexo feminino, idade M(DP)=42,9(11,6), educação M(DP)=9,6(4,7), anos diagnóstico M(DP)=12,8(9,7), classificação da doença M(DP)=6,6 (2,8)], recrutados nos hospitais centrais portugueses. Aplicando Modelos de Equações Estruturais e ajustando para variáveis sociodemográficas e clínicas, verificou-se que, pessoas mais otimistas, mais ativas e com uma melhor adesão aos tratamentos apresentam um melhor bem-estar geral, uma melhor saúde mental e um melhor bem-estar subjetivo; uma melhor adesão aos tratamentos contribui para uma melhor saúde física; melhor suporte social reflete-se numa melhor saúde mental; pessoas com mais espiritualidade apresentam uma melhor saúde física e uma melhor saúde mental. Estas conclusões contribuem para a definição de uma terapia que pode ajudar a uma melhor adaptação dos protocolos de tratamento para atender às necessidades dos doentes.

Ferramentas de gestão de redes e classificação de tráfego: estudo de caso

A gestão de redes informáticas converteu-se num fator vital para uma rede operar de forma eficiente, produtiva e lucrativa. A gestão envolve a monitorização e o controlo dos sistemas para que estes funcionam como o pretendido, ações de configuração, monitorização, reconfiguração dos componentes, são essenciais para o objetivo de melhorar o desempenho, diminuir o tempo de inatividade, melhor a segurança e efetuar contabilização. Paralelamente, a classificação de tráfego é um tema de bastante relevância em várias atividades relacionadas com as redes, tais como a previsão de QoS, segurança, monitorização, contabilização, planeamento de capacidade de backbones e deteção de invasão. A variação de determinados tipos de tráfego pode influenciar decisões técnicas na área da gestão de redes, assim como decisões políticas e sociais. Neste trabalho pretende-se desenvolver um estudo dos vários protocolos, ferramentas de gestão e de classificação de tráfego disponíveis para apoiar a atividade de gestão. O estudo efetuado terminou com a proposta e implementação de uma solução de gestão adequado a um cenário real bastante rico na diversidade de tecnologias e sistemas.

Optimization of immunocytochemistry in cytology: comparison of two protocols for fixation and preservation on cytospin and smear preparations

Objective: A new protocol for fixation and slide preservation was evaluated in order to improve the quality of immunocytochemical reactions on cytology slides. Methods: The quality of immunoreactions was evaluated retrospectively on 186 cytology slides (130 direct smears, 56 cytospins) prepared from different cytology samples. Ninety-three of the slides were air dried, stored at -20 °C and fixed in acetone for 10 minutes (Protocol 1), whereas the other 93 were immediately fixed in methanol at -20 °C for at least 30 minutes, subsequently protected with polyethylene glycol (PEG) and stored at room temperature (Protocol 2). Immunocytochemical staining, with eight primary antibodies, was performed on a Ventana BenchMark Ultra instrument using an UltraView Universal DAB Detection Kit. The following parameters were evaluated for each immunoreaction: morphology preservation, intensity of specific staining, background and counterstain. The slides were blinded and independently scored by four observers with marks from 0 to 20. Results: The quality of immunoreactions was better on methanol-fixed slides protected with PEG than on air-dried slides stored in the freezer: X¯ = 14.44 ± 3.58 versus X¯ = 11.02 ± 3.86, respectively (P < 0.001). Conclusion: Immediate fixation of cytology slides in cold methanol with subsequent application of PEG is an easy and straightforward procedure that improves the quality of immunocytochemical reactions and allows the storage of the slides at room temperature.

Integration of TCP/IP and PROFIBUS protocols

Recent technological developments are pulling fieldbus networks to support a new wide class of applications, such as industrial multimedia applications. These applications are usually supported by the widely used TCP/IP stack. It is thus essential to provide support to TCP/IP based applications, in fieldbus networks. This paper presents an effort that is being carried out to integrate the TCP/IP and PROFIBUS stacks, in order to support industrial multimedia applications, whilst guarantying the timing requirements of control-related traffic.

Supporting internet protocols in master-slave fieldbus networks

In this paper we describe how to integrate Internet Protocols (IP) into a typical hierarchical master-slave fieldbus network, supporting a logical ring token passing mechanism between master stations. The integration of the TCP/IP protocols in the fieldbus protocol rises a number of issues that must be addressed properly. In this paper we particularly address the issues related to the conveyance of IP fragments in fieldbus frames (fragmentation/de-fragmentation) and on how to support the symmetry inherent to the TCP/IP protocols in fieldbus slaves, which lack communication initiative.

Towards network-on-chip agreement protocols

Demands for functionality enhancements, cost reductions and power savings clearly suggest the introduction of multiand many-core platforms in real-time embedded systems. However, when compared to uni-core platforms, the manycores experience additional problems, namely the lack of scalable coherence mechanisms and the necessity to perform migrations. These problems have to be addressed before such systems can be considered for integration into the realtime embedded domain. We have devised several agreement protocols which solve some of the aforementioned issues. The protocols allow the applications to plan and organise their future executions both temporally and spatially (i.e. when and where the next job will be executed). Decisions can be driven by several factors, e.g. load balancing, energy savings and thermal issues. All presented protocols are analytically described, with the particular emphasis on their respective real-time behaviours and worst-case performance. The underlying assumptions are based on the multi-kernel model and the message-passing paradigm, which constitutes the communication between the interacting instances.

Two protocols without periodicity for the global and preemptive scheduling problem of multi-mode real-time systems upon multiprocessor Platforms

We consider the problem of scheduling a multi-mode real-time system upon identical multiprocessor platforms. Since it is a multi-mode system, the system can change from one mode to another such that the current task set is replaced with a new task set. Ensuring that deadlines are met requires not only that a schedulability test is performed on tasks in each mode but also that (i) a protocol for transitioning from one mode to another is specified and (ii) a schedulability test for each transition is performed. We propose two protocols which ensure that all the expected requirements are met during every transition between every pair of operating modes of the system. Moreover, we prove the correctness of our proposed algorithms by extending the theory about the makespan determination problem.