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At least 37 records · Page 2Linked to original sources
PubMed2005

Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses.

OBJECTIVE: Assessment of the usefulness of a neural model to predict which ovarian tumors are malignant. METHOD: Age, menopausal status, body mass index, grayscale and Doppler ultrasonographic features, as well as levels of specific markers (CA 125, tissue polypeptide specific antigen) were examined in 686 women with adnexal masses. The probability of malignancy was calculated using an artificial neural network software and the diagnostic efficiency of the received model was estimated using a receiver-operating characteristics (ROC) curve. RESULT: Of the 686 women, 431 (62.8%) had a benign and 255 (37.2%) had a malignant ovarian tumor. The significant malignancy predictors are age, menopausal status, maximum tumor diameter, internal wall structure of tumor, presence of septa and/or solid elements, tumor location, location of vessels, and blood flow indexes. The best network provided 96.0% sensitivity and 97.7% specificity. The area under the curve for the received model was 0.9716. CONCLUSIONS: An artificial neural network model based on clinical and ultrasonographic data allows to calculate the probability of tumor malignancy.

Adnexal Diseases↗
PubMed2005

A functional neural network computing some eigenvalues and eigenvectors of a special real matrix.

How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract some eigenvalues and eigenvectors of a special real matrix. After equivalent transforming the FNN into a complex differential equation and obtaining the analytic solution, the convergence properties of the FNN are analyzed. If the eigenvalue whose imaginary part is nonzero and the largest of all eigenvalues is unique, the FNN will converge to the eigenvector corresponding to this special eigenvalue with general nonzero initial vector. If all eigenvalues are real numbers or there are more than one eigenvalue whose imaginary part equals the largest, the FNN will converge to zero point or fall into a cycle procedure. Comparing with other neural networks designed for the same domain, the restriction to matrix is very slack. At last, three examples are employed to illustrate the performance of the FNN.

Algorithms↗
PubMed2001

Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography.

INTRODUCTION/OBJECTIVE: The purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians. METHODS AND MATERIAL: Materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications. RESULTS: The performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group. DISCUSSION AND CONCLUSION: Our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better predictive estimation of suspicious clustered microcalcifications and reduce the number of biopsies for non-palpable benign lesions.

Adult↗
PubMed2006

Using a speech perception neural network computer simulation to contrast neuroanatomic versus neuromodulatory models of auditory hallucinations.

A number of studies suggest that schizophrenia may arise from overzealous pruning of synapses that are an extension of normal developmental pruning during adolescence. Moreover, there has been a long history of studies suggesting that this disorder arises from alterations in the dopaminergic neuromodulatory systems. In order to further assess and compare these two hypotheses, a computer simulation of some aspects of speech perception was developed utilizing a recurrent, backpropagation model of working memory previously reported by Elman . This system was found to produce spontaneous percepts simulating hallucinated speech when the working memory component either was excessively pruned or when neuronal responses were modulated to simulatea hyperdopaminergic system. These hallucinogenic systems also demonstrated disruptions in processing input information when "phonetic information" was degraded. The perceptual performance of these systems were compared to that of actual hallucinating patients and normal controls while tracking (repeating while simultaneously listening to) speech that was phonetically degraded. We found that the neural network simulation producing the best match to speech tracking performance of human hallucinators was an overpruned system with compensatory hypodopaminergic adjustments. These data suggest that the primary pathophysiology of schizophrenia arises from curtailed connectivity in working memory systems and that dopaminergic alterations reflect secondary compensatory adjustments.

Adolescent↗
PubMed1991

Prediction of protein secondary structure by an enhanced neural network.

Computational model of neural network is used for prediction of secondary structure of globular proteins of known sequence. In contrast to earlier works some information about expected tertiary interactions were built in into the neural network. As a result the prediction accuracy was improved by 3% to 5%. Possible applications of this new approach are briefly discussed.

Amino Acid Sequence↗
PubMed1990

Computational neural networks: enhancing supervised learning algorithms via self-organization.

A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifier network. The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than conventional feedforward classifier networks when the decision regions are disjoint. This is attributed to the fact that the self-organization process allows internal units in the succeeding classifier network to be sensitive to a specific set of features in the input space at the outset of training.

Algorithms↗
PubMed1994

Visual field interpretation with a personal computer based neural network.

The Computer Assisted Touch Screen (CATS) and Computer Assisted Moving Eye Campimeter (CAMEC) are personal computer (PC)-based video-campimeters which employ multiple and single static stimuli on a cathode ray tube respectively. Clinical studies show that CATS and CAMEC provide comparable results to more expensive conventional visual field test devices. A neural network has been designed to classify visual field data from PC-based video-campimeters to facilitate diagnostic interpretation of visual field test results by non-experts. A three-layer back propagation network was designed, with 110 units in the input layer (each unit corresponding to a test point on the visual field test grid), a hidden layer of 40 processing units, and an output layer of 27 units (each one corresponding to a particular type of visual field pattern). The network was trained by a training set of 540 simulated visual field test result patterns, including normal, glaucomatous and neuro-ophthalmic defects, for up to 20,000 cycles. The classification accuracy of the network was initially measured with a previously unseen test set of 135 simulated fields and further tested with a genuine test result set of 100 neurological and 200 glaucomatous fields. A classification accuracy of 91-97% with simulated field results and 65-100% with genuine field results were achieved. This suggests that neural networks incorporated into PC-based video-campimeters may enable correct interpretation of results in non-specialist clinics or in the community.

Diagnosis, Computer-Assisted↗
PubMed1997

Comparison of intraocular lens computations using a neural network versus the Holladay formula.

PURPOSE: To compare the accuracy of intraocular lens (IOL) calculations using Holladay personalized calculations and a new method of trained neural networks. SETTING: A private ophthalmic practice. METHODS: We developed and trained a neural network to predict IOL powers using a personalized Holladay program and clinical data from 200 consecutive cases of one surgeon's results with one IOL. Clinical data included preoperative axial length, both keratometry values, anterior chamber depth, and human lens thickness. The neural network was trained to produce the actual postoperative refractive error, and the Holladay surgeon factor was continuously refined using the same results. After the network was successfully trained against the clinical data, it was used to compute IOL power in a double-masked study. Ninety-five patients were randomized between the Holladay personalized calculation and the neural network computation. There were no significant differences in age or preoperative refractive errors between the two groups. Manifest refractions were obtained during the masked period at least 6 weeks after surgery. RESULTS: Mean postoperative error from predicted refraction was +0.271 diopters (D) for the neural network group and -0.217 D for the Holladay personal group. Mean absolute error from predicted refraction was +0.63 D for the neural network group and +0.93 D for the Holladay personal group. There was a significant difference in postoperative refractive errors and mean absolute error between the two groups (P < .022; nonparametric Mann-Whitney test). An error of less than +/- 0.75 D was obtained by 72.5% of the neural network group and 50.0% of the Holladay group. CONCLUSIONS: The neural network prediction formula can improve IOL implantation calculations by tightening the variance of errors.

Cataract Extraction↗
PubMed1995

Cells and circuits contributing to functional properties in area V1 of macaque monkey cerebral cortex: bases for neuroanatomically realistic models.

Anatomical and physiological data obtained from investigations of area V1 of the macaque monkey visual cerebral cortex have been used in 3 models outlining possible circuitry underlying functional properties of the region. The 3 models use, respectively, a fully implemented computer neural network, a mathematical formulation of interactions in a descriptive model of anatomical circuitry and a purely descriptive account of circuitry that could underlie particular functions. The 1st 2 models involve as part of their design an interpolation principle where afferents of opposite physiological property establish spatially offset but adjacent terminal fields and the postsynaptic neurons' dendrites have a continuum of different degrees of overlap into the 2 afferent pools and therefore different synaptic weights from the 2 afferents; this creates a functional and spatial gradient of response properties in the postsynaptic neurons between the properties of the different sets of afferents. The 3rd model examines lateral excitatory and inhibitory interactions in such gradients. Model 1 addresses the transformation of distinct thalamic axon properties to a gradient of response properties in postsynaptic spiny stellate neurons in layer 4C of V1. Model 2 proposes circuitry producing orientation specificity in V1 that begins by generating specificity of responses to orthogonal orientations; this is achieved by means of orthogonally oriented lateral axon projections made by the layer 4C spiny stellate neurons; this is followed by generation of a full cycle of orientation specificities by means of pyramidal neuron dendritic overlap across spatially separated fields of spiny stellate neuron axons responding preferentially to orthogonal orientations. Model 3 describes a circuitry to explain inhibitory and facilitatory interactions observed to occur in single unit responses when the classical receptive field is stimulated concurrently with the surround region. All the proposed models make predictions that can be tested by further anatomical and physiological experiments in the real visual cortex.

Animals↗
PubMedDate not supplied

3-D components of a biological neural network visualized in computer generated imagery. II. Macular neural network organization.

Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural networks, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that 1) assemblies of cells analyse and distribute information with inbuilt redundancy, to improve reliability; 2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; 3) constrained randomness in connectivities contributes to adaptability; and 4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

Acoustic Maculae↗
PubMed2002

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra.

Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 +/- 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.

Bacteria↗
PubMed2000

Artificial neural networks: fundamentals, computing, design, and application.

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.

Humans↗
PubMed2003

A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish.

Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.

Animals↗
PubMed2000

Evaluating the frequency rate of hypomagnesemia in critically ill pediatric patients by using multiple regression analysis and a computer-based neural network.

OBJECTIVES: To determine the frequency rate of hypomagnesemia in patients admitted to the pediatric intensive care unit (ICU), and to identify subsets of patients (grouped by disease) who are at greatest risk of hypomagnesemia. We also compared a neural network model with multiple regression analysis to identify independent variables that would correlate with hypomagnesemia and to predict serum magnesium values in critically ill pediatric patients overall. DESIGN: Prospective, multicenter study. SETTING: Tertiary level medical/surgical pediatric ICUs. PATIENTS: Data were obtained at admission to the pediatric ICU for 463 patients from newborn to 18 yrs old who were admitted with a variety of surgical and nonsurgical conditions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Total serum magnesium values were obtained within the first 24 hrs after admission in 463 pediatric patients admitted to four pediatric ICUs. Hypomagnesemia (defined as total serum magnesium <0.75 mmol/L) was found in 51 (11%) of the 463 patients, with the highest frequency rate (72%) and lowest mean serum magnesium level (0.66 +/- 0.17 mmol/L) in patients admitted after surgery with extensive osseous involvement (spinal fusion and craniofacial reconstruction). To determine whether hypomagnesemia could be predicted on the basis of other laboratory and clinical criteria, multiple regression analysis was performed and showed age, weight, and albumin levels weakly associated (r2 = .14, p < .001) with magnesium levels within the different diagnostic groups. These data were used to produce a mathematical model able to predict magnesium levels within 5% of the actual values in 23% of patients. A neural network was also created to compare its predictive capabilities to those of the multiple regression model. Once trained on a random subset (85%) of the patient population, the neural network was able to predict magnesium levels to within 5% of actual values for 88% of the remaining 15% of patients, comparing favorably with the predictions derived from the multiple regression model. CONCLUSIONS: Hypomagnesemia is not uncommon (11%) in critically ill pediatric patients, but is very common (72%) in patients admitted after surgery for spinal fusion or craniofacial reconstruction. Patients who undergo surgery for correction of scoliosis and craniofacial anomalies should have serum magnesium levels monitored closely after surgery. In other patients, a neural network or multiple regression model could help predict which patients would be at risk of developing hypomagnesemia, thereby focusing testing on patients likely to benefit from such testing.

Adolescent↗
PubMed2003

General-purpose computation with neural networks: a survey of complexity theoretic results.

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.

Algorithms↗
PubMed2000

FPNA: interaction between FPGA and neural computation.

Neural networks are usually considered as naturally parallel computing models. But the number of operators and the complex connection graph of standard neural models can not be directly handled by digital hardware devices. More particularly, several works show that programmable digital hardware is a real opportunity for flexible hardware implementations of neural networks. And yet many area and topology problems arise when standard neural models are implemented onto programmable circuits such as FPGAs, so that the fast FPGA technology improvements can not be fully exploited. Therefore neural network hardware implementations need to reconcile simple hardware topologies with complex neural architectures. The theoretical and practical framework developed, allows this combination thanks to some principles of configurable hardware that are applied to neural computation: Field Programmable Neural Arrays (FPNA) lead to powerful neural architectures that are easy to map onto FPGAs, thanks to a simplified topology and an original data exchange scheme. This paper shows how FPGAs have led to the definition of the FPNA computation paradigm. Then it shows how FPNAs contribute to current and future FPGA-based neural implementations by solving the general problems that are raised by the implementation of complex neural networks onto FPGAs.

Computers↗
PubMed1990

Analysis of the secondary structure of the human immunodeficiency virus (HIV) proteins p17, gp120, and gp41 by computer modeling based on neural network methods.

A neural network computer program, trained to predict secondary structure of proteins by exposing it to matching sets of primary and secondary structures from a database, was used to analyze the human immunodeficiency virus (HIV) proteins p17, gp120, and gp41 from their amino acid sequences. The results are compared to those obtained by the Chou-Fasman analysis. Two alpha-helical sequences corresponding to the putative fusigenic domain and to the transmembrane domain of gp41 could be predicted, as well as a possible binding site between p17 and gp41. On the basis of the secondary structure predictions, a three-dimensional model of p17 was constructed. This model was found to represent a stable conformation by an analysis using an energy-minimization program. The model predicts that p17 is attached to the membrane only by the acylated N-terminus, in analogy with the N-terminus of the gag protein of other retroviruses and also with the src oncogene protein p60src. The intracellular C-terminal part of gp41 may act as a receptor by electrostatic interaction with p17.

Algorithms↗