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PubMed1993

Classification of electrocardiographic ST-T segments--human expert vs artificial neural network.

Artificial neural networks, which can be used for pattern recognition, have recently become more readily available for application in different research fields. In the present study, the use of neural networks was assessed for a selected aspect of electrocardiographic (ECG) waveform classification. Two experienced electrocardiographers classified 1000 ECG complexes singly on the basis of the configuration of the ST-T segments into eight different classes. ECG data from 500 of these ST-T segments together with the corresponding classifications were used for training a variety of neural networks. After this training process, the optimum network correctly classified 399/500 (79.8%) ST-T segments in the separate test set. This compared with a repeatability of 428/500 (85.6%) for one electrocardiographer. Conventional criteria for the classification of one type of ST-T abnormality had a much worse performance than the neural network. It is concluded that neural networks, if carefully incorporated into selected areas of ECG interpretation programs, could be of value in the near future.

Electrocardiography↗
PubMed1998

Epidemiologic interpretation of artificial neural networks.

Multilayer neural networks have been faulted for functioning as "black boxes" and for failing to assess the relative importance of the input factors. The aim of this paper is to illustrate how neural networks can classify individuals. The authors investigated the role of weights in the formation of neural networks' decision surfaces and decision regions. The data used were from a case-control study. Two strong determinants of case status were used as input "neurons." Zero, three, and five hidden neurons were used to explore the effect of the number of hidden neurons on the decision surfaces and regions. Mapping of input and output spaces revealed that three hidden neurons were insufficient to fully discriminate cases from controls. Five hidden neurons may be optimal, but at the cost of possible over-fitting. The more complex neural networks were very effective at defining regions of uniform risk in the plane of the initial covariates, and at assigning risk levels. The authors speculate that neural networks will prove useful in epidemiologic problems that require pattern recognition or complicated classification techniques, and that they will be unfavorable in problems that involve distinct effects of distinguishable predictors.

Artificial Intelligence↗
PubMed1997

Coronary artery bypass risk prediction using neural networks.

BACKGROUND: Neural networks are nonparametric, robust, pattern recognition techniques that can be used to model complex relationships. METHODS: The applicability of multilayer perceptron neural networks (MLP) to coronary artery bypass grafting risk prediction was assessed using The Society of Thoracic Surgeons database of 80,606 patients who underwent coronary artery bypass grafting in 1993. The results of traditional logistic regression and Bayesian analysis were compared with single-layer (no hidden layer), two-layer (one hidden layer), and three-layer (two hidden layer) MLP neural networks. These networks were trained using stochastic gradient descent with early stopping. All prediction models used the same variables and were evaluated by training on 40,480 patients and cross-validation testing on a separate group of 40,126 patients. Techniques were also developed to calculate effective odds ratios for MLP networks and to generate confidence intervals for MLP risk predictions using an auxiliary "confidence MLP." RESULTS: Receiver operating characteristic curve areas for predicting mortality were approximately 76% for all classifiers, including neural networks. Calibration (accuracy of posterior probability prediction) was slightly better with a two-member committee classifier that averaged the outputs of a MLP network and a logistic regression model. Unlike the individual methods, the committee classifier did not overestimate or underestimate risk for high-risk patients. CONCLUSIONS: A committee classifier combining the best neural network and logistic regression provided the best model calibration, but the receiver operating characteristic curve area was only 76% irrespective of which predictive model was used.

Bayes Theorem↗
PubMed1997

Prediction of trauma mortality using a neural network.

A neural network is a computerized construct consisting of input neurons (which process input data) connected to hidden neurons (to mathematically manipulate values they receive from all the input neurons) connected to output neurons (to output a prediction). Neural networks are created and trained via multiple iterations over data with known results. In 1993, 897 trauma patients were either declared dead in the emergency room (ER; 76 cases), admitted to the intensive care unit (427 cases, 36 deaths), or taken directly to the operating room (394 cases, 29 deaths). Using only data available from the ER, a neural network was created, and 628 cases were randomly selected for training. After 268 iterations, the network was trained to correctly predict death or survival in all 628 cases. This trained network was then tested on the other 269 cases without our providing the death or survival result. Its overall accuracy was 91 per cent (244 of 269 cases). It was able to predict correctly 60 per cent (12 of 20 cases) of the postoperative or post-intensive care unit admission deaths and 90 per cent (26 of 29 cases) of the deaths in the ER. Computerized neural networks can accurately predict a trauma patient's fate based on inital ER presentation. The theory and use of neural networks in predicting clinical outcome will be presented.

Age Factors↗
PubMed1994

Interpretation of automated perimetry for glaucoma by neural network.

PURPOSE: Neural networks were trained to interpret the visual fields from an automated perimeter. The authors evaluated the reliability of the trained neural networks to discriminate between normal eyes and eyes with glaucoma. METHODS: Inclusion criteria for glaucomatous and normal eyes were the intraocular pressure and the appearance of the optic nerve; previous visual fields were not used. The authors compared the backpropagation learning method used by automated neural networks to those used by two specialists in glaucoma to classify the central 24 degrees automated perimetric visual fields from 60 normal and 60 glaucomatous eyes. RESULTS: The glaucoma experts and a trained two-layered network were each correct at approximately 67%. The average sensitivity of this test was 59% for the two glaucoma specialists and 65% for the two-layered network. The corresponding specificities were 74% and 71% for the specialists and the two-layered network, respectively. The experts and the network were in agreement about 74% of the time, which indicated no significant disagreement between the methods of testing. Feature analysis with a one-layered network determined the most important visual field positions. CONCLUSIONS: The authors conclude that a neural network can be taught to be as proficient as a trained reader in interpreting visual fields for glaucoma.

Adult↗
PubMed1999

Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network.

On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.

Journal Article↗
PubMed1992

Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks.

A neural network classification scheme was developed that enables a picture archiving and communications system workstation to determine the correct orientation of posteroanterior or anteroposterior chest images. This technique permits thoracic images to be displayed conventionally when called up on the workstation, and therefore reduces the need for reorientation of the image by the observer. Feature data were extracted from 1,000 digitized chest radiographs and used to train a two-layer neural network designed to classify the image into one of the eight possible orientations for a posteroanterior chest image. Once trained, the neural network identified the correct image orientation in 888 of 1,000 images that had not previously been seen by the neural network. Of the 112 images that were incorrectly classified, 106 were mirror images of the correct orientation, whereas only 6 actually had the caudal-cranial axis aligned incorrectly. The causes for misalignment are discussed.

Humans↗
PubMed1991

Markovian neural networks.

The neural network that efficiently and nearly optimally solves difficult optimization problems is defined. The convergence proof for the Markovian neural network that asynchronously updates its neurons' states is also presented. The comparison of the performance of the Markovian neural network with various combinatorial optimization methods in two domains is described. The Markovian neural network is shown to be an efficient tool for solving optimization problems.

Cybernetics↗
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↗
PubMed1992

A demonstration that breast cancer recurrence can be predicted by neural network analysis.

Neural Network Analysis, a form of artificial intelligence, was successfully used to predict the clinical outcome of node-positive breast cancer patients. A Neural Network was trained to predict clinical outcome using prognostic information from 1008 patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as length of clinical followup, relapse status, and time of relapse. The ability of the trained Network to determine relapse probability was then validated in a separate set of 960 patients. The Neural Network was as powerful as Cox Regression Modeling in identifying breast cancer patients at high and low risk for relapse.

Axilla↗
PubMed2001

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks.

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.

Electrophoresis, Capillary↗
PubMed2005

Neural network dynamics.

Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.

Action Potentials↗
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↗
PubMedDate not supplied

Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.

Artificial neural networks have been applied to the differentiation of actual "true" clusters from normal parenchymal patterns and also to the differentiation of actual clusters from false-positive clusters as reported by a computerized scheme for the detection of microcalcifications in digital mammograms. The differentiation was carried out in both the spatial and frequency domains. The performance of the neural networks was evaluated quantitatively by means of receiver operating characteristic (ROC) analysis. It was found that the networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false-positive clusters of microcalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme. A large, comprehensive training database is needed for neural networks to perform reliably in clinical situations.

Breast Diseases↗
PubMed1997

Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis.

Neural network analysis was applied for early diagnosis/exclusion of acute myocardial infarction (AMI), prediction of infarct size, and estimation of "time from onset of infarction." Eighty-eight patients admitted within 8 h after onset of chest pain were included. Blood samples for measurement of myoglobin, creatine kinase isoform MB, and troponin T were obtained every 30 min during the first 3 h and then after successively longer intervals. Data from 50 patients were used to train a set of neural network components of a decision support system. The performance of the system was evaluated and compared with experienced clinicians for the remaining 38 patients. The computer system detected myocardial infarction and predicted infarct size earlier than the clinicians, but did not differ significantly in terms of diagnostic sensitivity, specificity, and predictive values when disregarding time for diagnosis. With a cross-validation procedure the cumulated sensitivities of the computer system for the first five measurements were estimated to be (mean +/- 2SEM, n = 100): 0.77 +/- 0.03, 0.89 +/- 0.02, 0.94 +/- 0.02, 0.97 +/- 0.01, and 0.99 +/- 0.01, respectively, with corresponding cumulated specificities between 0.93 +/- 0.01 and 0.91 +/- 0.01. We concluded that neural network analysis of serial measurements of biochemical markers might provide useful support for the early assessment of patients with suspected AMI.

Aged↗
PubMed2006

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

Artificial Intelligence↗
PubMed1990

Improvements in protein secondary structure prediction by an enhanced neural network.

Computational neural networks have recently been used to predict the mapping between protein sequence and secondary structure. They have proven adequate for determining the first-order dependence between these two sets, but have, until now, been unable to garner higher-order information that helps determine secondary structure. By adding neural network units that detect periodicities in the input sequence, we have modestly increased the secondary structure prediction accuracy. The use of tertiary structural class causes a marked increase in accuracy. The best case prediction was 79% for the class of all-alpha proteins. A scheme for employing neural networks to validate and refine structural hypotheses is proposed. The operational difficulties of applying a learning algorithm to a dataset where sequence heterogeneity is under-represented and where local and global effects are inadequately partitioned are discussed.

Artificial Intelligence↗
PubMed2003

Use of algorithms as determinants for individual patient decision making: national comprehensive cancer network versus artificial neural networks.

The National Comprehensive Cancer Network (NCCN) developed a series of algorithms based on expert opinion to guide the treatment of patients with prostate cancer. These algorithms define acceptable treatment options according to the risk of disease recurrence and the life expectancy of the patient. However, practicing clinicians are expected to use medical judgment when making actual treatment decisions. Many clinical and pathologic variables affect patient prognosis, which, in turn, influences the treatment and surveillance of patients. Artificial neural networks (ANNs) offer promise for improving the predictive value of traditional statistical modeling. ANN models have been designed that predict risk of lymph node spread and capsular involvement during disease staging, risk of disease recurrence after prostatectomy, and overall and cause-specific survival. This article provides a review of guidelines, such as NCCN and ANN, used for the management of prostate cancer and suggests that group-level recommendations based on these algorithms or other decision trees may misrepresent individual patient preferences for treatment. Patients and their clinicians need to consider available prognostic information, including clinical status, pathologic variables, and comorbidities, and then select a reasonable treatment approach that maximizes outcome and quality of life according to the preferences of each patient.

Algorithms↗