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At least 469 records · Page 26Linked to original sources
PubMed2005

Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination.

Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points. We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.

Algorithms↗
PubMed2004

Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems.

The technology supporting the analysis of human motion has advanced dramatically. Past decades of locomotion research have provided us with significant knowledge about the accuracy of tests performed, the understanding of the process of human locomotion, and how clinical testing can be used to evaluate medical disorders and affect their treatment. Gait analysis is now recognized as clinically useful and financially reimbursable for some medical conditions. Yet, the routine clinical use of gait analysis has seen very limited growth. The issue of its clinical value is related to many factors, including the applicability of existing technology to addressing clinical problems; the limited use of such tests to address a wide variety of medical disorders; the manner in which gait laboratories are organized, tests are performed, and reports generated; and the clinical understanding and expectations of laboratory results. Clinical use is most hampered by the length of time and costs required for performing a study and interpreting it. A "gait" report is lengthy, its data are not well understood, and it includes a clinical interpretation, all of which do not occur with other clinical tests. Current biotechnology research is seeking to address these problems by creating techniques to capture data rapidly, accurately, and efficiently, and to interpret such data by an assortment of modeling, statistical, wave interpretation, and artificial intelligence methodologies. The success of such efforts rests on both our technical abilities and communication between engineers and clinicians.

Biomechanical Phenomena↗
PubMed1997

A WWW-accessible knowledge base for the interpretation of hepatitis serologic tests.

HEPAXPERT is a knowledge-based system that interprets the results of routine serologic tests for infection with hepatitis A and B viruses. The following tests are included: hepatitis A virus anti-bodies (anti-HAV), IgM antibodies to the hepatitis A virus (IgM anti-HAV), hepatitis A virus (HAV in stool, hepatitis B surface antigen (HBsAg) and antibodies (qualitative anti-HBs, quantitative anti-HBs titre), antibodies to hepatitis B core antigen (anti-HBc and IgM anti-HBc), and hepatitis B envelope antigen (HBeAg) and antibodies (anti-HBe). HEPAXPERT/WWW--an implementation of HEPAXPERT-III for WWW--can be reached by URL http://www.swun.com/hepax of the World Wide Web. After selecting HEPAXPERT/WWW, serologic test results can be entered and will be transferred as an e-mail message for subsequent interpretation which is done off-line with HEPAXPERT-III. The textual interpretation is sent back via e-mail. Each qualitative test for hepatitis A and B antibodies and antigens may produce one of four possible results: positive, negative, borderline, and not tested. To cover the resulting 64 (A) and 57344 (B) combinations of findings, the knowledge base of HEPAXPERT/WWW contains 16 rules for hepatitis A and 131 rules for hepatitis B serology interpretation. This basic knowledge is structured such that all possible combinations of findings can be interpreted and there is no overlap in the premises underlying the rules. The reports that the system automatically generates include: (a) the transferred results of the tests; (b) a detailed analysis of the results, including virus exposure, immunity, stage of illness, prognosis, infectiousness, and vaccination recommendation; and (c) optional: an ID to distinguish the origin of the interpretation requests.

Artificial Intelligence↗
PubMed2006

Shape statistics variational approach for the outer contour segmentation of left ventricle MR images.

Segmentation of left ventricles is one of the important research topics in cardiac magnetic resonance (MR) imaging. The segmentation precision influences the authenticity of ventricular motion reconstruction. In left ventricle MR images, the weak and broken boundary increases the difficulty of segmenting the outer contour precisely. In this paper, we present an improved shape statistics variational approach for the outer contour segmentation of left ventricle MR images. We use the Mumford-Shah model in an object feature space and incorporate the shape statistics and an edge image to the variational framework. The introduction of shape statistics can improve the segmentation with broken boundaries. The edge image can enhance the weak boundary and thus improve the segmentation precision. The generation of the object feature image, which has homogenous "intensities" in the left ventricle, facilitates the application of the Mumford-Shah model. A comparison of mean absolute distance analysis between different contours generated with our algorithm and that generated by hand demonstrated that our method can achieve a higher segmentation precision and a better stability than various approaches. It is a semiautomatic way for the segmentation of the outer contour of the left ventricle in clinical applications.

Algorithms↗
PubMed2004

On the structure and initial parameter identification of Gaussian RBF networks.

We consider the efficient initialization of structure and parameters of generalized Gaussian radial basis function (RBF) networks using fuzzy decision trees generated by fuzzy ID3 like induction algorithms. The initialization scheme is based on the proposed functional equivalence property of fuzzy decision trees and generalized Gaussian RBF networks. The resulting RBF network is compact, easy to induce, comprehensible, and has acceptable classification accuracy with stochastic gradient descent learning algorithm.

Algorithms↗
PubMedDate not supplied

[A new information technology for system diagnosis of functional activity of human organs].

The goal of this work was to consider a new diagnostic technology based on analysis of objective information parameters of functional activity and interaction of normal and pathologically changed human organs. The technology is based on the use of very low power millimeter (EHF) radiation emitted by human body and other biological objects in the process of vital activity. The importance of consideration of the information aspect of vital activity from the standpoint of the theory of functional systems suggested by P. K. Anokhin is emphasized. The suggested information technology is theoretically substantiated. The capabilities of the suggested technology for diagnosis, as well as the difficulties of its practical implementation caused by very low power of electromagnetic fields generated by human body, are discussed. It is noted that only use of modern radiophysical equipment together with new software based on specially developed algorithms made it possible to construct a medical EHF diagnostic system for effective implementation of the suggested technology. The system structure, functions of its components, the examination procedure, and the form of representation of diagnostic information are described together with the specific features of applied software based on the principle of maximal objectivity of analysis and interpretation of the results of diagnosis on the basis of artificial intelligence algorithms. The diagnostic capabilities of the system are illustrated by several examples.

Biophysics↗
PubMed2005

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗
PubMed2005

Automatic registration and fusion of ultrasound with CT for radiotherapy.

We present a framework for rigid registration of a set of B-mode ultrasound images to a CT scan in the context of Radiotherapy planning. Our main focus is on deriving an appropriate similarity measure based on the physical properties and artifacts of ultrasound. A combination of a weighted Mutual Information term, edge correlation, clamping to the skin surface and occlusion detection is able to assess the alignment of structures in ultrasound images and simulated slices generated from the CT data. Hence a set of ultrasound images, whose relative transformations are given by a magnetic tracking device, can be registered automatically to the CT scan. We validated our methods on neck data of patients with head and neck tumors and cervical lymph node metastases.

Algorithms↗
PubMed1990

DataVoice: a microcomputer-based general purpose voice-controlled data-collection system.

An integrated system of hardware and software has been developed to combine the input of coordinate data from a digitizing pad with voice input for object identification or classification. The menu and voice-controlled software generates a sequential ASCII file which contains an object identification section and a data section. Rules for data entry and analysis programs have been developed for several applications. These are being tested and include general stereological analyses, bone, kidney and skin histomorphometry, boundary analyses, neuron classification and malignancy grading.

Artificial Intelligence↗
PubMed2006

Multiple exemplar-based facial image retrieval using independent component analysis.

In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases.

Algorithms↗
PubMed2005

A fuzzy ontology and its application to news summarization.

In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.

Abstracting and Indexing↗
PubMed2004

Artificial neural networks for the prediction of response to interferon plus ribavirin treatment in patients with chronic hepatitis C.

Combined therapy using Interferon alfa (IFN) and Ribavirin (RIB) represents the standard treatment in patients with chronic hepatitis C. However, the percentage of responders to this regimen is still low, while its cost and side effects are elevated. Therefore, the possibility to predict patient's response to the above treatment is of paramount importance. The progress in the field of informatics and its large use for decision making has led to the development of novel techniques related to the so-called Artificial Intelligence, even including artificial neural networks (ANNs). In chronic viral hepatitis data are lacking. By means of an artificial neural network (ANN), 300 patients treated with IFN plus RIB were retrospectively analyzed with the aim to predict the response to the treatment. One hundred patients resulted responders and 200 non-responders at the end of treatment and during the follow up. For evaluating the prediction of treatment response, six ANNs with 16 neurons of input, an hidden layer with 7 neurons and an output layer with one neuron were utilized. The ANN model generated a positive predictive value (i.e. posterior probability of treatment response) ranging from 57% to 75% while the negative one (i.e. posterior probability of no response to treatment) was comprised between 52% and 71%. The highest level of diagnostic accuracy was 70%. In conclusion, ANNs appear to be a promising tool in the prediction of treatment response in patients with chronic hepatitis C. However, additional prospective studies are necessary to ultimately validate this predictive method.

Adolescent↗
PubMedDate not supplied

Molecular level investigations of the inter- and intramolecular interactions of pH-responsive artificial triblock proteins.

Intelligent materials that can undergo physical gelation in response to environmental stimuli have potential impacts in the bioengineering and biomedical fields where the entrapment of cellular or molecular species is desired. Here, we utilize atomic force microscopy (AFM) to perform molecular level investigations of designer artificial proteins that undergo physical gelation. These are engineered as triblock copolymers with independent interchain binding and solvent retention functions, namely, two terminal leucine zipper-like peptide sequences and a central alanylglycine rich sequence, respectively. AFM force measurements between probes and surfaces functionalized with molecules of this triblock protein revealed adhesive interactions that increased in average force and frequency as the pH was lowered from pH 11.2 to 7.4 to 4.5, reflecting an increase in the numbers of interacting molecular strands. In bulk solution, lowering the pH results in a viscous liquid to gel transition. The modular design of the triblock protein was also exploited for single molecule force spectroscopy investigations, which revealed altered intramolecular interactions in response to changes in pH. An increased understanding of the inter- and intramolecular forces involved in biomolecule driven gelation processes is not only of great fundamental interest in the study of the biomolecular systems involved but may also prove key in enabling the rational design of new generations of intelligent hydrogel systems.

Amino Acid Sequence↗
PubMed2005

Predictions of patterns of response to artificial selection in lines derived from natural populations.

The pattern of response to artificial selection on quantitative traits in laboratory populations can tell us something of the genetic architecture in the natural population from which they were derived. We modeled artificial selection in samples drawn from natural populations in which variation had been maintained by recurrent mutation, with genes having an effect on the trait, which was subject to real stabilizing selection, and a pleitropic effect on fitness (the joint-effect model). Natural selection leads to an inverse correlation between effects and frequencies of genes, such that the frequency distribution of genes increasing the trait has an extreme U-shape. In contrast to the classical infinitesimal model, an early accelerated response and a larger variance of response among replicates were predicted. However, these are reduced if the base population has been maintained in the laboratory for some generations by random sampling prior to artificial selection. When multiple loci and linkage are also taken into account, the gametic disequilibria generated by the Bulmer and Hill-Robertson effects are such that little or no increase in variance and acceleration of response in early generations of artificial selection are predicted; further, the patterns of predicted responses for the joint-effect model now become close to those of the infinitesimal model. Comparison with data from laboratory selection experiments shows that, overall, the analysis did not provide clear support for the joint-effect model or a clear case for rejection.

Artificial Intelligence↗
PubMed2003

Automatic concept extraction from spoken medical reports.

OBJECTIVE: The objective of this project is to investigate methods whereby a combination of speech recognition and automated indexing methods substitute for current transcription and indexing practices. METHODS: We based our study on existing speech recognition software programs and on NOMINDEX, a tool that extracts MeSH concepts from medical text in natural language and that is mainly based on a French medical lexicon and on the UMLS. For each document, the process consists of three steps: (1) dictation and digital audio recording, (2) speech recognition, (3) automatic indexing. The evaluation consisted of a comparison between the set of concepts extracted by NOMINDEX after the speech recognition phase and the set of keywords manually extracted from the initial document. The method was evaluated on a set of 28 patient discharge summaries extracted from the MENELAS corpus in French, corresponding to in-patients admitted for coronarography. RESULTS: The overall precision was 73% and the overall recall was 90%. Indexing errors were mainly due to word sense ambiguity and abbreviations. A specific issue was the fact that the standard French translation of MeSH terms lacks diacritics. A preliminary evaluation of speech recognition tools showed that the rate of accurate recognition was higher than 98%. Only 3% of the indexing errors were generated by inadequate speech recognition. DISCUSSION: We discuss several areas to focus on to improve this prototype. However, the very low rate of indexing errors due to speech recognition errors highlights the potential benefits of combining speech recognition techniques and automatic indexing.

Abstracting and Indexing↗
PubMed2005

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data.

MOTIVATION: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. RESULTS: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. CONTACT: leixu@jhu.edu.

Algorithms↗
PubMed2004

Weighting features to recognize 3D patterns of electron density in X-ray protein crystallography.

Feature selection and weighting are central problems in pattern recognition and instance-based learning. In this work, we discuss the challenges of constructing and weighting features to recognize 3D patterns of electron density to determine protein structures. We present SLIDER, a feature-weighting algorithm that adjusts weights iteratively such that patterns that match query instances are better ranked than mismatching ones. Moreover, SLIDER makes judicious choices of weight values to be considered in each iteration, by examining specific weights at which matching and mismatching patterns switch as nearest neighbors to query instances. This approach reduces the space of weight vectors to be searched. We make the following two main observations: (1) SLIDER efficiently generates weights that contribute significantly in the retrieval of matching electron density patterns; (2) the optimum weight vector is sensitive to the distance metric i.e. feature relevance can be, to a certain extent, sensitive to the underlying metric used to compare patterns.

Absorptiometry, Photon↗