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At least 1,675 records · Page 93Linked to original sources
PubMed2004

Neural network analysis of anal sphincter repair.

PURPOSE: Prediction of success after anterior sphincter repair for incontinence is difficult. Standard multivariate analysis techniques have only 75 to 80 percent accuracy. Artificial intelligence, including artificial neural networks, has been used in the analysis of complex clinical data and has proved to be successful in predicting the outcome of other surgical procedures. Using a neural network algorithm, we have assessed the probability of success after anterior sphincter repair. METHODS: Prospective anorectal physiology data of 72 patients undergoing anterior sphincter repair was collected between 1995 and 1999. Complete data sets of 75 percent of the series were used to train an artificial neural network; the remaining 25 percent were used for data validation. The output was continence grading, ranging from 0 to 4 (worse to continent). RESULTS: The outcome at 3, 6, and 12 months postoperatively was obtained and assessed. The best correlation between actual data value and artificial neural network value was found at 12 months (r = 0.931; P = 0.0001). Clear correlations also were found at three months (r = 0.898; P = 0.0001) and six months (r = 0.742; P = 0.002). Results of applying a net to details excluding pudendal nerve latency were poor. CONCLUSIONS: Artificial neural networks are more accurate (93 percent correlation) than standard statistics (75 percent) when applied to the prediction of outcome after anterior sphincter repair. This assessment also confirms the usefulness of pudendal latency in the prediction of anterior sphincter repair outcome. The results obtained highlight the obvious usefulness of artificial neural networks, which could now be used in a prospective evaluation for application of the technique.

Anal Canal↗
PubMed2007

Artificial neural network: predicted vs observed survival in patients with colonic cancer.

PURPOSE: An Internet-web-based artificial neural network has been developed for practicing clinical oncologists and medical researchers as part of an ongoing program designed for the implementation of advanced neural networks for prognostic estimates and eventually for management/treatment decisions in individual patients with colonic cancer. METHODS: An interdisciplinary team of academic oncologists and physicists has configured and implemented a Partial Logistic Artificial Neural Network and trained it to predict cancer-related survival in patients with confirmed colorectal cancer by using a database (1,558 patients) made available for the study by the Information & Statistics Division of National Health Service Scotland. The reliability of the trained network was evaluated against Kaplan-Meier observed survival plots of a random sample of 300 patients not used in the training but forming part of the same data set. RESULTS: The predicted survival curves obtained as the output from the artificial neural network showed close agreement with observed actual survival rates of a cohort of 300 patients with four grades of risk of dying from the cancer within five years of diagnosis. CONCLUSIONS: The web-based Partial Logistic Artificial Neural Network system accurately predicts survival after staging and treatment of colonic cancer. It can be made web-accessible where it is powerful enough to serve hundreds of users simultaneously.

Aged↗
PubMed2005

Genetic neural network modeling of the selective inhibition of the intermediate-conductance Ca2+ -activated K+ channel by some triarylmethanes using topological charge indexes descriptors.

Selective inhibition of the intermediate-conductance Ca(2+)-activated K(+ )channel (IK (Ca)) by some clotrimazole analogs has been successfully modeled using topological charge indexes (TCI) and genetic neural networks (GNNs). A neural network monitoring scheme evidenced a highly non-linear dependence between the IK (Ca) blocking activity and TCI descriptors. Suitable subsets of descriptors were selected by means of genetic algorithm. Bayesian regularization was implemented in the network training function with the aim of assuring good generalization qualities to the predictors. GNNs were able to yield a reliable predictor that explained about 97% data variance with good predictive ability. On the contrary, the best multivariate linear equation with descriptors selected by linear genetic search, only explained about 60%. In spite of when using the descriptors from the linear equations to train neural networks yielded higher fitted models, such networks were very unstable and had relative low predictive ability. However, the best GNN BRANN 2 had a Q ( 2 ) of LOO of cross-validation equal to 0.901 and at the same time exhibited outstanding stability when calculating 80 randomly constructed training/test sets partitions. Our model suggested that structural fragments of size three and seven have relevant influence on the inhibitory potency of the studied IK (Ca) channel blockers. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (KSOM) built using the inputs of the best neural network predictor.

Algorithms↗
PubMed2005

Feedback inhibition and throughput properties of an integrate-and-fire-or-burst network model of retinogeniculate transmission.

Computational modeling has played an important role in the dissection of the biophysical basis of rhythmic oscillations in thalamus that are associated with sleep and certain forms of epilepsy. In contrast, the dynamic filter properties of thalamic relay nuclei during states of arousal are not well understood. Here we present a modeling and simulation study of the throughput properties of the visually driven dorsal lateral geniculate nucleus (dLGN) in the presence of feedback inhibition from the perigeniculate nucleus (PGN). We employ thalamocortical (TC) and thalamic reticular (RE) versions of a minimal integrate-and-fire-or-burst type model and a one-dimensional, two-layered network architecture. Potassium leakage conductances control the neuromodulatory state of the network and eliminate rhythmic bursting in the presence of spontaneous input (i.e., wake up the network). The aroused dLGN/PGN network model is subsequently stimulated by spatially homogeneous spontaneous retinal input or spatio-temporally patterned input consistent with the activity of X-type retinal ganglion cells during full-field or drifting grating visual stimulation. The throughput properties of this visually-driven dLGN/PGN network model are characterized and quantified as a function of stimulus parameters such as contrast, temporal frequency, and spatial frequency. During low-frequency oscillatory full-field stimulation, feedback inhibition from RE neurons often leads to TC neuron burst responses, while at high frequency tonic responses dominate. Depending on the average rate of stimulation, contrast level, and temporal frequency of modulation, the TC and RE cell bursts may or may not be phase-locked to the visual stimulus. During drifting-grating stimulation, phase-locked bursts often occur for sufficiently high contrast so long as the spatial period of the grating is not small compared to the synaptic footprint length, i.e., the spatial scale of the network connectivity.

Action Potentials↗
PubMedDate not supplied

A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.

Action Potentials↗
PubMed2006

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Algorithms↗
PubMed1995

Interpretation of nonstress tests by an artificial neural network.

OBJECTIVE: Our purpose was to evaluate an artificial neural network in the interpretation of nonstress tests. STUDY DESIGN: A nonlinear artificial neural network trained by backpropagation was taught to interpret records of nonstress tests by two learning sets. The first set contained nonstress tests that were similarly interpreted by three human experts; the second set contained a subset of nonstress tests that led to interobserver disagreement. Both "raw" fetal heart rate and uterine contraction data and 17 quantified variables obtained by automated computer analysis were introduced to the input layer. After training, the network was tested by presenting it with input patterns to which it had not been exposed. The performance of the system was examined in relation to the human expert. RESULTS: After training the neural network with the first set, a sensitivity of 88.9% and a false-positive rate of 4.3% were obtained at testing. When the learning and test set contained records that led to interobserver disagreement, a sensitivity of 86.7% and a false-positive rate of 19.7% were obtained. Sixty percent of fetal heart rate records interpreted as abnormal by the neural network were interpreted likewise by the human experts. CONCLUSIONS: The results obtained are encouraging in that the neural network could discriminate between normal and abnormal nonstress tests. Further evaluation of this new technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate records.

Algorithms↗
PubMed1994

Simulated neural networks to predict outcomes, costs, and length of stay among orthopedic rehabilitation patients.

Our purpose was to develop a set of simulated neural networks that would predict functional outcomes, length of stay, and costs among orthopedic patients admitted to an inpatient rehabilitation hospital. We used retrospective data for a sample of 387 patients between the ages of 60 and 89 who had been admitted to a single rehabilitation facility over a period of 12 months. Using age and data on functional capacity at admission from the Functional Independence Measure, we were successful in constructing networks that were 86%, 87%, and 91% accurate in predicting functional outcome, length of stay, and costs to within +/- 15% of the actual value. In each case the accuracy of the network exceeded that of a multiple regression equation using the same variables. Our results show the feasibility of using simulated neural networks to predict rehabilitation outcomes, and the advantages of neural networks over conventional linear models. Networks of this kind may be of significant value to administrators and clinicians in predicting outcomes and resource usage as rehabilitation hospitals are faced with capitation and prospective payment schemes.

Activities of Daily Living↗
PubMed1992

A parallel implementation of the backward error propagation neural network training algorithm: experiments in event identification.

An artificial neural-network-based (ANN) event detection and alarm generation system has been developed to aid clinicians in the identification of critical events commonly occurring in the anesthesia breathing circuit. To detect breathing circuit problems, the system monitored CO2 gas concentration, gas flow, and airway pressure. Various parameters were extracted from each of these input waveforms and fed into an artificial neural network. To develop truly robust ANNs, investigators are required to train their networks on large training data sets, requiring enormous computing power. We implemented a parallel version of the backward error propagation neural network training algorithm in the widely portable parallel programming language C-Linda. A maximum speedup of 4.06 was obtained with six processors. This speedup represents a reduction in total run-time from 6.4 to 1.5 h. By reducing the total run time of the computation through parallelism, we were able to optimize many of the neural network's initial parameters. We conclude that use of the master-worker model of parallel computation is an excellent method for speeding up the backward error propagation neural network training algorithm.

Algorithms↗
PubMed1992

Neural networks for classification of ECG ST-T segments.

The usefulness of neural networks for pattern recognition in electrocardiographic (ECG) ST-T segments was assessed. Two thousand ST-T segments from the 12-lead ECG were visually classified singly into 7 different groups. The material was divided into a training set and a test set. Computer-measured ST-T data for each element in the training set, paired with the corresponding classification, was input to various configurations of software-based neural networks during a learning process. Thereafter, the networks correctly classified 90-95% of the individual ST-T segments in the test set. The importance of the size and composition of the training set in determining the performance of a network was clearly demonstrated. In conclusion, neural networks can be used for classification of ST-T segments. If carefully incorporated into a conventional ECG interpretation program, neural networks may well be of value for automated ECG interpretation in the near future.

Electrocardiography↗
PubMed1996

Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel.

Two multilayer neural networks were designed to discriminate vigilance states (waking, paradoxical sleep, and non-REM sleep) in the rat using a single parieto-occipital EEG derivation. After filtering (bandwidth 3.18-25 Hz) and digitization at 512 HZ, the EEG signal was segmented into eight second epochs. Five variables (three statistical, two temporal) were extracted from each epoch. The first network computed an epoch by epoch classification, while the second network also utilized contextual information from contiguous epochs. A specific postprocessing procedure was developed to enhance the vigilance state discrimination of the neural networks designed and especially paradoxical sleep state estimation. The classifications made by the networks (with or without the postprocessing procedure) for six rats were compared to these made by two human experts using EMG and EEG informations on 63,000 epochs. High rates of agreement (> 90%) between humans and neural networks classifications were obtained. In view of its development possibilities and its applicability to other signals, this method could prove of value in biomedical research.

Animals↗
PubMed1993

Acoustical detection of coronary occlusions using neural networks.

A nonlinear neural network classifier was applied to noninvasive acoustic detection of coronary artery disease; the classifier included a feature vector, derived from diastolic heart sounds, and a multi-layered network trained by the backpropagation. The feature vector is based on the linear prediction coefficients of the autoregressive method after an adaptive line enhancement method was used as the input pattern to the neural network. One hundred and twelve recordings (70 abnormal, 42 normal) were studied and the network was trained on a randomly chosen set of six abnormal and six normal patients. It was tested on a database consisting of 100 recordings to which it had not been exposed. The network correctly identified 50 of the 64 patients with coronary artery disease and 32 of the 36 patients without any coronary artery occlusions. These results showed that this neural network is capable of distinguishing normal patients from abnormal patients. In addition, the diagnostic capability of this approach is much better than any other available noninvasive approach.

Coronary Disease↗
PubMed1994

Neural-network contributions in biotechnology.

In the mid-1980s, widespread interest in research into artificial neural networks re-emerged following a period of reduced research funding. The much wider availability and the increased power of computing systems, together with new areas of research, is expanding the range of potential application. The main reason for this is that the potential to describe the characteristics of extremely complex systems accurately has been attributed to this methodology. This article examines the contribution of various network methodologies to bioprocess modelling, control and pattern recognition. Industrial processes can benefit from the application of feedforward networks with sigmoidal activation functions, radial basis function networks and autoassociative networks. The contribution that neural networks can make to biochemical and microbiological scientific research is also reviewed briefly.

Animals↗
PubMed1994

Neural [correction of Neutral] networks for control, identification and diagnosis.

Advances in the theory and technology of artificial neural networks provide the potential for new approaches to the problems of control, identification, and diagnosis for large, complex systems. However, these approaches must be validated for specific applications before they can be exploited effectively. Because of the unique capabilities they offer, neural networks should play an important role in space exploration systems operations. After a brief introduction to neural networks is presented, some applications of neural networks to identification and control of space systems are described and discussed. They span the spectrum of relatively straightforward to rather complex applications. An explanation of how neural networks can be applied to such important tasks as fault diagnosis and accommodation is presented. Neural networks are shown to be part of the hierarchy of intelligent control where a higher order decision element monitors and supervises lower order elements for sensing and actuation.

Algorithms↗
PubMed1997

XNBC: a simulation tool. Application to the study of neural coding using hybrid networks.

XNBC is a software package for simulating biological neural networks. Two neuron models are available, a leaky integrator model and an ion-conductance model. Inputs to the simulated neurons can be provided by experimental data stored in files, allowing the creation of 'hybrid' networks. Graphic tools are used to describe the modeled neurons as well as the network. Neuron and network parameters can be modified during the simulation, to mimic electrical stimulations and drugs action. The temporal evolution of the network and of selected neurons can be visualized. A point process, frequency or dynamic analysis of the simulator output can be performed. The successive stages of the creation of a hybrid network are explained.

Computer Graphics↗
PubMed1994

Application of backpropagation neural networks to diagnosis of breast and ovarian cancer.

Neural network programs have been developed in an attempt to improve the diagnosis of breast and ovarian cancer using a group of laboratory tests and the age of the patient. The laboratory tests employed in this study include albumin, cholesterol, HDL-cholesterol, triglyceride, apolipoproteins A1 and B, NMR linewidth (the Fossel Index) and a tumor marker (i.e., CA 15-3 or CA 125). The breast cancer study involved 104 patients (45 malignant and 59 benign subjects). The ovarian cancer study involved 98 individuals (35 malignant, 36 benign and 27 control subjects). Methods are outlined for identification of the most influential input parameters and optimization of network structure and training. Network characteristics were contrasted with the test results of the appropriate serum tumor marker assay. For the breast cancer study, the best neural network program, using six input parameters, had a sensitivity of only 55.6% and a specificity of 72.9%. The tumor marker CA 15-3 alone gave results of 61.3% and 64.4%, respectively. For the ovarian cancer study, the best neural network program, using six input parameters, had a sensitivity of 80.6% and a specificity of 85.5%. The tumor marker CA 125 alone gave results of 77.8% and 82.3%, respectively. These methods provide an objective approach to neural network optimization and parameter selection applicable to other data bases of clinical and laboratory data.

Biomarkers, Tumor↗
PubMed1993

Using neural networks to model personality development.

A neural network approach to modeling the development of personality traits through social learning is presented. From the more general model the special case of a network mapping four situation dimensions (input neurons) into seven dimensional personality traits (output neurons) is described. This network is allowed to learn with input/output sets representing conditions suspected of leading to a borderline personality disorder. The network's ability to learn these pattern pairs is demonstrated. The trained network is then presented with new input (situational) patterns and is shown to respond to these new situations with output patterns consistent with a borderline personality disorder. The neural network model is thus shown to have important advantages over other personality models in that it can predict what situations will produce shifts in personality traits, for example from active to passive. This model provides a quantitative and reproducible framework within which to discover and test theories of personality development. It is hoped that it will extend our ability to predict human behavior.

Humans↗
PubMed1993

Multiple disorder diagnosis with adaptive competitive neural networks.

Backpropagation neural networks have repeatedly been used for diagnostic problem-solving, but have not been demonstrated to work well when multiple disorders are present. We hypothesized that letting nodes in a backpropagation neural network compete to be part of a diagnostic solution would produce better performance than the use of existing backpropagation methods. To test this hypothesis, we derived an error backpropagation learning rule that can be used with competitive units (competitive backpropagation). Artificial neural networks were then trained using both this new learning rule and standard error backpropagation on a specific medical diagnosis problem: identification of the location of damage in the brain given a set of examination findings. Training samples included solely 'prototypical' cases where a single location of damage is present. The trained networks were then tested with atypical cases where the manifestations of more than one disorder were present or only a single manifestation was present. Networks employing competition among units were found to perform qualitatively better with these multiple-disorder cases than standard networks and also to perform better on single-manifestation cases. The reasons for this are explained. The competitive backpropagation learning rule described here provides a promising new tool for adaptive diagnostic problem-solving.

Diagnosis, Computer-Assisted↗