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At least 1,207 records · Page 67Linked to original sources
PubMed2001

A use of a neural network to evaluate contrast enhancement curves in breast magnetic resonance images.

For the diagnosis of breast cancer using magnetic resonance imaging (MRI), one of the most important parameters is the analysis of contrast enhancement. A three-dimensional MR sequence is applied before and five times after bolus injection of paramagnetic contrast medium (Gd-DTPA). The dynamics of absorption are described by a time/intensity enhancement curve, which reports the mean intensity of the MR signal in a small region of interest (ROI) for about 8 minutes after contrast injection. The aim of our study was to use an artificial neural network to automatically classify the enhancement curves as "benign" or "malignant." We used a classic feed-forward back-propagation neural network, with three layers: five input nodes, two hidden nodes, and one output node. The network has been trained with 26 pathologic curves (10 invasive carcinoma [K], two carcinoma-in-situ [DCIS], and 14 benign lesion [B]). The trained network has been tested with 58 curves (36 K, one DCIS, 21 B). The network was able to correctly identify the test curves with a sensitivity of 76% and a specificity of 90%. For comparison, the same set of curves was analyzed separately by two radiologists (a breast MR expert and a resident radiologist). The first correctly interpreted the curves with a sensitivity of 76% and a specificity of 90%, while the second scored 59% for sensitivity and 90% for specificity. These results demonstrate that a trained neural network recognizes the pathologic curves at least as well as an expert radiologist. This algorithm can help the radiologist attain rapid and affordable screening of a large number of ROIs. A complete automatic computer-aided diagnosis support system should find a number of potentially interesting ROIs and automatically analyze the enhancement curves for each ROI by neural networks, reporting to the radiologist only the potentially pathologic ROIs for a more accurate, manual, repeated evaluation.

Breast↗
PubMed1999

The Finnmark study. Social support, social network and mental distress in a prospective population study.

BACKGROUND: Earlier studies on the direct effect of social support and social network upon mental health have mostly been done in cross-sectional studies and the results have been conflicting. METHODS: The direct effect of social network and social support upon mental distress was tested in population-based health surveys conducted in 1987, 1990 and 1993. The population consisted of all persons aged 40-62 years and a random sample of persons aged between 20 and 39 years living in five small municipalities in Finnmark, the northernmost county in Norway. The attendance rates were 77%, 74% and 70% for the three health surveys, respectively. All those who had attended the health surveys more than once and answered the key questions about mental distress, social support and social network were selected for analyses (966 persons who attended both the two first health surveys and 1425 persons attending both the two last surveys). A theoretical full cross-lagged panel model was used to search for the predictive effects of time 1 variables (mental distress, social network and social support) upon corresponding subsequent variables. RESULTS: Mental distress, social support and social network at time 1 strongly predicted the corresponding variable at the next time point. Emotional support at time 1 exerted a weak positive effect upon mental distress on the next time point, explaining approximately 1% of the variance. Neither the social network nor instrumental support at time 1 predicted degree of mental distress at the subsequent time point. CONCLUSION: We conclude that social network and social support have very little direct predictive effect upon mental distress 3 years later in this population.

Adult↗
PubMed2003

Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory.

We propose a neural network model for a category-association task. By simulating the model, neuronal relevance of cortical interactions to recalling long-term memory was investigated. The model consists of the left and right hemispheres, each of which has IT (inferotemporal cortex) and PC (prefrontal cortex) networks. Information about visual features and their categories were encoded into point attractors of the IT and PC networks, respectively. In the task, the IT network of the right hemisphere was stimulated with a cue feature. After a delay period, the IT network of the left hemisphere was simultaneously stimulated with the choice feature and an irrelevant feature. The cue and choice features belong to the same category, while the irrelevant feature belongs to another category. To complete the task, the IT network must select the point attractor corresponding to the choice feature. We demonstrate that the top-down pathway (PC-to-IT) triggers the retrieval of long-term memory of the choice feature from the IT, and the bottom-up pathway (IT-to-PC) contributes to the maintenance of the retrieved memory during the delay period. The key mechanism for the retrieval and maintenance of that memory is the dynamic linkage of attractors across separate cortical networks. We show that a single hemisphere is sufficient for the memory retrieval, but it is advantageous to use the two hemispheres because the retrieved memory is thereby retained with greater reliability until the brain chooses the choice feature.

Action Potentials↗
PubMed2000

Novel approach to acoustical voice analysis using artificial neural networks.

Perceptual rating scales are widely used for the assessment of voice quality. These ratings may be influenced by the individual experience of the listener. Thus, researchers have turned to acoustical measures which may eventually correlate with voice quality. In this study we tested whether multivariate statistics, combined with artificial neural networks, could identify patterns of acoustic voice parameters corresponding to a widely used perceptual rating scale. In a multicenter study with 31 raters, voice samples of 117 individuals with or without voice disorders were perceptually rated. The RBH index, consisting of a 4-point scale of roughness, breathiness, and hoarseness, was used. Voice samples were then analyzed with an acoustical feature extraction and classified using amultivariate regression tree analysis with the perceptual ratings as a priori information. Artificial neural networks were trained to selected acoustic parameters having high "relative importance" in the regression trees. Mean classification accuracies were around 30% with topographic feature maps (trained with Learning Vector Quantization algorithm) and 65-85% with feedforward networks (trained with RProp algorithm). Based on the best-fitting results with feedforward networks, a classification system (computer program) consisting of 50 simultaneous working networks was developed. Using this program, the classification matched 40% of the a prori values in both R and B domains. In 65% they matched at least in one domain. These accuracies are within the range reported by other authors using artificial neural networks in biology and clinical medicine. Thus, the results encourage further research of feedforward networks for acoustic voice analysis.

Humans↗
PubMed2002

A pragmatic discussion on establishing a multicenter digital imaging network.

Multicenter clinical trials for therapy evaluation of rare diseases are necessary. A digital imaging network improves the ability to share information between collaborating institutions for adrenoleukodystrophy. The DICOM 3.0 standard is used to move images over the Internet from contributing sites to the central clinical database and on to the reviewing physicians' workstations. Patient confidentiality and data integrity are ensured during transmission using virtual private network technology. Fifteen sites are participating in the network. Of these sites, 6 use the proposed protocol. The other 9 sites have either security policy issues or technical considerations that dictate alternative protocols. Network infrastructure, Internet access, image management practices, and security policies vary significantly between sites. Successful implementation of a multicenter digital imaging network requires flexibility in the implementation of network connectivity. Flexibility increases participation as well as complexity of the network.

Adrenoleukodystrophy↗
PubMed2005

Two-cell to N-cell heterogeneous, inhibitory networks: precise linking of multistable and coherent properties.

Inhibitory networks are now recognized as being the controllers of several brain rhythms. However, experimental work with inhibitory cells is technically difficult not only because of their smaller percentage of the neuronal population, but also because of their diverse properties. As such, inhibitory network models with tight links to the experimental data are needed to understand their contributions to population rhythms. However, mathematical analyses of network models with more than two cells is challenging when the cellular models involve biophysical details. We use bifurcation analyses and simulations to show that two-cell analyses can quantitatively predict N-cell (N = 20, 50, 100) network dynamics for heterogeneous, inhibitory networks. Interestingly, multistable states in the two-cell system are manifest as different and distinct coherent network patterns in the N-cell networks for the same parameter sets.

Action Potentials↗
PubMed2006

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons.

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme.

Action Potentials↗
PubMed2006

Advanced course for doctors as Departmental IT Network Administrators in anesthesia and intensive care units.

The design and administration of a departmental computer network (Local Area Network) in anesthesiology and intensive care offer the opportunity to manage clinical information and control the work-flow. To improve the local network, after basic design, intelligence is necessary to maintain its efficiency. For this reason the role of a medical administrator of the network is fundamental because he is a qualified figure who recognizes the most important characteristics that a network must have, knows the users of the system, represents a valid consultant for the technician that has to build the network, and is able to face possible breakdowns. This paper illustrates the structure of a course to train a medical network administrator in anesthesiology and critical care.

Anesthesia Department, Hospital↗
PubMed1995

Neural network prediction of nonstress test results: how often should we perform nonstress tests?

OBJECTIVE: Our purpose was to predict outcomes and optimal intervals for nonstress tests of term gravid women with neural networks. STUDY DESIGN: We studied 100 normal term patients whose 30-minute nonstress tests, performed on 5 consecutive days, were computer analyzed for the following elements: fetal heart rate baseline, variability, signal loss, accelerations (> 15 beats/min), and decelerations. The training set used 65 patients; the testing, 35 patients. Nonstress test data (days 1 to 4) were inputs; day 5 data were training patterns. Networks for each nonstress test element used Brainmaker Macintosh 1.0 (California Scientific Software, Nevada City, Calif.) trained to 0.12 tolerance. Actual fetal heart rate elements and their daily differences were compared with predictions by the networks and multiple regressions. RESULTS: There was little difference between networks using daily or alternate-day inputs for predicting test performance on day 5; networks using test intervals > 2 days could not be trained to tolerance. Long-term fetal heart rate variation was the nonstress test element best predicted. Daily differences networks provided better prediction of all day 5 data than did actual daily values networks or multiple regression formulas. CONCLUSIONS: Baseline long-term fetal heart rate variability seems to be the most predictable fetal heart rate element over time and should merit more consideration in overall fetal testing. Fetal heart rate elements are not easily predicted by any method for intervals longer than 2 days. Using longer test intervals might run a greater risk for unanticipated changes in nonstress test outcomes, even when fetal condition is normal.

Female↗
PubMed1993

Identification of peak V in brainstem auditory evoked potentials with neural networks.

A feature extractor for determining the latency of peak V in brainstem auditory evoked potentials (BAEPs) is presented in this paper. A feature extractor that combines artificial neural networks with an algorithmic approach is presented. It consists of a series of small neural networks that have to make simple decisions. Each neural network decides what part of the input pattern contains the peak, and the algorithm passes that part of the pattern to the next neural network; in this way the size of the input patterns decreases during the process, and the last neural network determines the exact location of the peak. An optimal configuration of neural networks could determine the latencies of peak V in all synthetic evoked potentials correctly. With real evoked potentials, the networks yield results that comply with the opinion of a human expert in 80 +/- 6% of the cases.

Adult↗
PubMed1993

Pattern recognition of the electroencephalogram by artificial neural networks.

A back-propagation network was trained to recognize high voltage spike-and-wave spindle (HVS) patterns in the rat, a rodent model of human petit mal epilepsy. The spontaneously occurring HVSs were examined in 137 rats of the Fisher 344 and Brown Norway strains and their F1, F2 and backcross hybrids. Neocortical EEG and movement of the rat were recorded for 12 night hours in each animal and analog data were filtered (low cut: 1 Hz; high cut: 50 Hz) and sampled at 100 Hz with 12 bit precision. A training data set was generated by manually marking durations of HVS epochs in 16 representative animals selected from each group. Training data were presented to back-propagation networks with variable numbers of input, hidden and output cells. The performance of different types of networks was first examined with the training samples and then the best configuration was tested on novel sets of the EEG data. FFT transformation of EEG significantly improved the pattern recognition ability of the network. With the most effective configuration (16 input; 19 hidden; 1 output cells) the summed squared error dropped by 80% as compared with that of the initial random weights. When testing the network with new patterns the manual and automatic evaluations were compared quantitatively. HVSs which were detected properly by the network reached 93-99% of the manually marked HVS patterns, while falsely detected events (non-HVS, artifacts) varied between 18% and 40%. These findings demonstrate the utility of back-propagation networks in automatic recognition of EEG patterns.

Animals↗
PubMed1996

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Algorithms↗
PubMed1995

On the training and performance of high-order neural networks.

An extensive study was undertaken on the architecture, training, and properties of neural networks of order higher than 1. The formulation of the training of high-order neural networks as a nonlinear associative recall problem provides the basis for their optimal least squares training. The simplicity of the outer-product rule motivates the study of the approximation of optimal least squares training by the outer-product rule and the effect of the network order on the efficiency of this approximation. Neural networks with composite key patterns are subsequently proposed as the natural generalization of neural networks of order higher than 1. The properties of this class of neural networks are revealed by studying their optimal least squares training and its relationship with the outer-product rule. The performance of neural networks with composite key patterns is analytically evaluated by studying their capacity. The properties and performance bounds provided by the analytical study are verified through experimental results.

Least-Squares Analysis↗
PubMed1994

Modelling of human acute toxicity from physicochemical properties and non-vertebrate acute toxicity of the 38 organic chemicals of the MEIC priority list by PLS regression and neural network.

Linear and non-linear modelling of human acute toxicity (as human lethal concentrations; HLCs) of the 38 organic chemicals from the 50 priority compounds of the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme was investigated. The models obtained were derived either from a set of 23 physicochemical properties of the compounds or from their acute toxicities to five aquatic non-vertebrates together with the physicochemical properties. For the linear type, modelling was performed using a partial least square projection to latent structures (PLS) regression method; for the non-linear models, both PLS regression and neural network were utilized. A neural network using a combination of backpropagation and cascade-correlation algorithms was applied in this study. The results generally reveal a slightly better predictive performance of the models obtained from PLS regression than those obtained from neural networks. However, the model composed of physicochemical properties (PC-model) from the trained neural network using a back propagation algorithm with pruning technique proved superior to that trained with a combination of backpropagation and cascade-correlation algorithms after leave-one-out cross-validation. The predictive power of the PC-models, whether linear or non-linear, was comparable with that of the corresponding models consisting of both structural descriptors and the ecotoxicological tests (ECOPC-models), except for the battery (ECOPC-model) from the neural network. The composition of the 'best' PLS and neural network models points to the importance of the combination of physicochemical properties reflecting lipophilicity, size, volume, intermolecular binding forces and electronic properties of the molecule. All the aquatic non-vertebrate tests are shown to be essential in explaining human acute toxicity. However, the degree of contribution differed, with the crustacean (Artemia salina) and the bacterial (Microtox) bioassays being more important to the linear and non-linear PLS models, whereas the crustacean (Artemia salina and Streptocephalus proboscideus) tests, and the rotifer (Brachionus calyciflorus) assay were important to the neural network models. The organochlorine (lindane) and bipyridinium (paraquat) pesticides were common outliers in all the models. Moreover, the latter two compounds and the organophosphate (malathion) pesticide were also common outliers in all ECOPC-models. Other types of pesticides, however, fit the models. The predicted HLCs of a number of non-pesticides, including some chlorinated compounds, also deviated from the observed HLCs by more than one order of magnitude.

Algorithms↗
PubMed1996

Artificial neural networks: current status in cardiovascular medicine.

Artificial neural networks are a form of artificial computer intelligence that have been the subject of renewed research interest in the last 10 years. Although they have been used extensively for problems in engineering, they have only recently been applied to medical problems, particularly in the fields of radiology, urology, laboratory medicine and cardiology. An artificial neural network is a distributed network of computing elements that is modeled after a biologic neural system and may be implemented as a computer software program. It is capable of identifying relations in input data that are not easily apparent with current common analytic techniques. The functioning artificial neural network's knowledge is built on learning and experience from previous input data. On the basis of this prior knowledge, the artificial neural network can predict relations found in newly presented data sets. In cardiology, artificial neural networks have been successfully applied to problems in the diagnosis and treatment of coronary artery disease and myocardial infarction, in electrocardiographic interpretation and detection of arrhythmias and in image analysis in cardiac radiography and sonography. This report focuses on the current status of artificial neural network technology in cardiovascular medical research.

Cardiology↗
PubMed1996

Neural network analysis of the EMG interference pattern.

This paper investigates the performance of artificial neural networks for analysing and classifying EMG signals from healthy subjects and patients with myopathic and neuropathic disorders. EMG interference patterns (IP) were recorded under maximum voluntary contraction from the right biceps of a total of 50 subjects. Parameters were obtained from the signals using recognized quantification techniques including turns analysis, small segments analysis and frequency analysis. Supervised networks examined were an improved backpropagation network (IBPN), a radial basis network (RBN), and a learning vector quantization network (INQ). Supervised networks using different combinations of parameters from turns analysis and small segments analysis gave diagnostic yields of 60-80%. Combinations using frequency analysis parameters produced similar results. The performance of unsupervised Self-Organising Feature Maps (SOFM) was generally lower than that of the supervised networks. Including personal data (sex and age) did not improve the overall performance.

Biomedical Engineering↗
PubMed2005

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses.

RATIONALE AND OBJECTIVE: To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms. MATERIALS AND METHODS: Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis. RESULTS: The area under the ROC curve Az (+/-SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity. CONCLUSION: There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.

Breast Neoplasms↗
PubMed2026

Brain network alterations underlying cue reactivity and craving in abstinent methamphetamine users: a systematic review of functional MRI findings.

BACKGROUND: Methamphetamine use disorder (MUD) is marked by intense craving and high relapse risk, often triggered by drug-related cues. Functional magnetic resonance imaging (fMRI) provides key insight into the neural basis of this cue reactivity, implicating large-scale brain networks for reward, motivation, and control. Yet, findings remain inconsistent across studies due to differences in task design, abstinence duration, and participant characteristics. OBJECTIVE: This systematic review synthesises evidence on how abstinence influences brain network alterations underlying cue reactivity and craving in methamphetamine users, integrating task-based and resting-state fMRI findings within leading neurobiological models of addiction. METHODS: A systematic search of PubMed, Scopus, Web of Science, and Ovid was conducted up to August 10, 2025, following PRISMA 2020 guidelines. Eligible fMRI studies examined cue reactivity or craving in abstinent methamphetamine users. Data were extracted on activation, connectivity, and brain-behaviour associations, and synthesised narratively. RESULTS: Task-based studies revealed heightened activation across reward, salience, and control networks during cue exposure, which diminished as parietal and executive control systems re-engaged with longer abstinence. Resting-state findings showed disrupted intrinsic connectivity among default mode, salience, and frontoparietal networks, reflecting persistent imbalances linked to craving and use severity. CONCLUSION: fMRI evidence shows that MUD is marked by network-level disruption linking reward, salience, and control systems. Task-based findings reveal strong cue reactivity in reward circuits, while resting-state data show persistent imbalance among default mode and control networks. With abstinence, partial restoration of network integrity emerges, highlighting both vulnerability and opportunities for targeted, recovery-based interventions.

Humans↗