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PubMed1992

Predicting protein secondary structure content. A tandem neural network approach.

A priori knowledge of secondary structure content can be of great use in theoretical and experimental determination of protein structure. We present a method that uses two computer-simulated neural networks placed in "tandem" to predict the secondary structure content of water-soluble, globular proteins. The first of the two networks, NET1, predicts a protein's helix and strand content given information about the protein's amino acid composition, molecular weight and heme presence. Because NET1 contained more adjustable parameters (network weights) than learning examples, this network experienced problems with memorization, which is the inability to generalize onto new, never-seen-before examples. To overcome this problem, we designed a second network, NET2, which learned to determine when NET1 was in a state of generalization. Together, these two networks produce prediction errors as low as 5.0% and 5.6% for helix and strand content, respectively, on a set of protein crystal structures bearing little homology to those used in network training. A comparison between three other methods including a multiple linear regression analysis, a non-hidden-node network analysis and a secondary structure assignment analysis reveals that our tandem neural network scheme is, indeed, the best method for predicting secondary structure content. The results of our analysis suggest that the knowledge of sequence information is not necessary for highly accurate predictions of protein secondary structure content.

Databases, Bibliographic↗

PubMed2001

Estimation of spinal deformity in scoliosis from torso surface cross sections.

STUDY DESIGN: Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects. OBJECTIVES: To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network. SUMMARY OF BACKGROUND DATA: Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown. METHODS: A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan-radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0-58 degrees ): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made. RESULTS: Torso-spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6 degrees in 63% of the test data set and was able to distinguish a Cobb angle greater than 30 degrees with a sensitivity of 1.0 and specificity of 0.75. CONCLUSIONS: Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.

Adolescent↗

PubMed1997

Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks.

Recent studies have explored characteristics of brain tumors by means of magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well as tumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy. Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) based only on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics, age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using the same information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.

Astrocytoma↗

PubMed1999

Recognition of seed varieties using a temporal organisation map analysis of electrophoretic images.

This paper presents a method for seed varieties recognition using one-dimensional electrophoresis gels. It employs a neural network basically constituted of temporal organisation maps (TOM). The TOM model is a neural net which was initially developed for speech recognition. It can be trained to recognise words in speech by reference to the sound pattern over a sequence of time steps. Electrophoresis creates a set of bands in the gel, caused by migration of protein from the seed. Each seed variety generates a characteristic pattern. The bands are made visible by staining. They can then be imaged and digitised to create an input to a TOM, which treats the variation with distance along the lane in the same way as the time sequence for which it was originally employed. In this way the characteristic signature of a seed variety can be recognised. A set of 50 images--each containing 10 to 15 lanes--was used to train and test the performance of a neural network in recognising 75 cereal varieties. The network could achieve a recognition rate of 98%, provided that the gel was not distorted or cracked during heating or drying. Details of the design and training of the network are given.

Artifacts↗

PubMed1996

Determination of dependent and independent communication paths using neural networks.

Efficient and timely computation of routing algorithms is very important for proper operation of multihop packet radio networks. When a network operates in the presence of jammers (a hostile environment) additional constraints must be considered when computing the routing tables. Insertion of those constraints in routing algorithms enables the determination of dependent and independent paths between source and destination nodes, thus fixing lower limits on the number of external jamming sources required to sever a transmission. An important measure of network reliability is also provided. This paper proposes a solution to the routing problem through the implementation of a Hopfield network and demonstrates that by proper selection of an energy function we can solve the problem rapidly and reliably.

Algorithms↗

PubMed1992

Computer recognition of brain stem auditory evoked potential wave V by a neural network.

A neural network simulator was used for the recognition of the presence and location of the peak of wave V of the brain stem auditory evoked potential (BAEP) test. Waveforms selected from BAEPs performed in the last 4 years at the University of Pittsburgh Presbyterian University Hospital were digitized and sampled, and the resulting amplitudes were normalized. A training set was composed of the waveforms resulting from the stimulation of 50 ears. The normalized amplitudes were used as the initial activation values for the networks. The desired outputs (the target locations for wave V) were represented in the output layer by setting the output element, which corresponded to the target location and its immediate neighbors, to high activation levels, and all the remaining output units to zero activity. Two network architectures, differing only in the hidden unit layer, with 40 and 16 hidden units, respectively, were trained by using standard back-propagation. Several trials from different starting points were performed for each architecture. The best network, found after 60 epochs (3,000 presentations), was able to correctly identify 17 of 20 cases (85%) from a set of test cases that were independent from the training set.

Evaluation Studies as Topic↗

PubMed1994

Cell suicide in the developing nervous system: a functional neural network model.

A computational model of programmed cell death (PCD) in the nervous system is described. A neurobiologically realisable method for identifying and removing the least useful cells from a network is developed, and it is shown by simulation that an artificial neural network can solve difficult problems efficiently if it is given more neurons initially than it needs subsequently. The least useful neurons die off gradually after learning is complete, and the learned solution can then be maintained with a smaller number of units than were needed for initial learning. The research suggests a functional role for PCD, and how self-limiting PCD could be achieved in real neural systems.

Apoptosis↗

PubMed1999

Neural network prediction of obstructive sleep apnea from clinical criteria.

STUDY OBJECTIVES: Clinical prediction models for the diagnosis of obstructive sleep apnea (OSA) have lacked the accuracy necessary to confidently replace polysomnography (PSG). Artificial neural networks are computer programs that can be trained to predict outcomes based on experience. This study was conducted to test the hypothesis that a generalized regression neural network (GRNN) could accurately classify patients with OSA from clinical data. STUDY DESIGN: Retrospective review. SETTING: Regional sleep referral center. PATIENTS: Randomly selected records of patients referred for possible OSA. MEASUREMENTS: The neural network was trained using 23 clinical variables from 255 patients, and the predictive performance was evaluated using 150 other patients. RESULTS: The prevalence of OSA in this series of 405 patients (293 men and 112 women) was 69%. The trained GRNN had an accuracy of 91.3% (95% confidence interval [CI], 86.8 to 95.8). The sensitivity was 98.9% for having OSA (95% CI, 96.7 to 100), and the specificity was 80% (95% CI, 70 to 90). The positive predictive value that the patient would have OSA was 88.1% (95% CI, 81.8 to 94.4), whereas the negative predictive value that the patient would not have OSA (if so classified) was 98% (95% CI, 94 to 100). CONCLUSIONS: Appropriately trained GRNN has the ability to accurately rule in OSA from clinical data, and GRNN did not misclassify patients with moderate to severe OSA. In this study, use of the neural network could have reduced the number of PSG studies performed. Prospective validation of the neural network for the diagnosis of OSA is now required.

Adolescent↗

PubMed1994

Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.

A computer-aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme. The shift-invariant neural network is a multilayer back-propagation neural network with local, shift-invariant interconnections. The advantage of the shift-invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift-invariant neural network was evaluated by means of a jackknife (or holdout) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve (Az) of 0.91. Approximately 55% of false-positive ROIs were eliminated without any loss of the true-positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three-layer, feed-forward neural network. The effect of the network structure on the performance of the shift-invariant neural network is also studied.

Biophysical Phenomena↗

PubMed1999

Discontinuities in recurrent neural networks.

This article studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net function and a sigmoid-like continuous activation function. We introduce arithmetic networks as ARNN augmented with a few simple discontinuous (e.g., threshold or zero test) neurons. We argue that even with weights restricted to polynomial time computable reals, arithmetic networks are able to compute arbitrarily complex recursive functions. We identify many types of neural networks that are at least as powerful as arithmetic nets, some of which are not in fact discontinuous, but they boost other arithmetic operations in the net function (e.g., neurons that can use divisions and polynomial net functions inside sigmoid-like continuous activation functions). These arithmetic networks are equivalent to the Blum-Shub-Smale model, when the latter is restricted to a bounded number of registers. With respect to implementation on digital computers, we show that arithmetic networks with rational weights can be simulated with exponential precision, but even with polynomial-time computable real weights, arithmetic networks are not subject to any fixed precision bounds. This is in contrast with the ARNN that are known to demand precision that is linear in the computation time. When nontrivial periodic functions (e.g., fractional part, sine, tangent) are added to arithmetic networks, the resulting networks are computationally equivalent to a massively parallel machine. Thus, these highly discontinuous networks can solve the presumably intractable class of PSPACE-complete problems in polynomial time.

Algorithms↗

PubMed1996

Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

AIMS: To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images. METHODS: 147 diabetic and 32 normal images were captured from a fundus camera, stored on computer, and analysed using a back propagation neural network. The network was trained to recognise features in the retinal image. The effects of digital filtering techniques and different network variables were assessed. 200 diabetic and 101 normal images were then randomised and used to evaluate the network's performance for the detection of diabetic retinopathy against an ophthalmologist. RESULTS: Detection rates for the recognition of vessels, exudates, and haemorrhages were 91.7%, 93.1%, and 73.8% respectively. When compared with the results of the ophthalmologist, the network achieved a sensitivity of 88.4% and a specificity of 83.5% for the detection of diabetic retinopathy. CONCLUSIONS: Detection of vessels, exudates, and haemorrhages was possible, with success rates dependent upon preprocessing and the number of images used in training. When compared with the ophthalmologist, the network achieved good accuracy for the detection of diabetic retinopathy. The system could be used as an aid to the screening of diabetic patients for retinopathy.

Diabetic Retinopathy↗

PubMed1999

Neural network in communication with medical computer system.

The paper presents a concept of an experimental module designed to recognise spoken utterances that cover a limited range of words indispensable in dialogues with computer medical systems. Research into the recognition of spoken words by a module based on artificial neural network is described. Usefulness of the obtained results for surgery-assisting multimedia systems and for a patient simulator supporting medical education of students in case history-taking and diagnosing is also discussed.

Artificial Intelligence↗

PubMed1994

An associative memory that can form hypotheses: a phase-coded neural network.

Nonlinear associative memories as realized, e.g., by Hopfield nets are characterized by attractor-type dynamics. When fed with a starting pattern, they converge to exactly one of the stored patterns which is supposed to be most similar. These systems cannot render hypotheses of classification, i.e., render several possible answers to a given classification problem. Inspired by von der Malsburg's correlation theory of brain function, we extend conventional neural network architectures by introducing additional dynamical variables. Assuming an oscillatory time structure of neural firing, i.e., the existence of neural clocks, we assign a so-called phase to each formal neuron. The phases explicitly describe detailed correlations of neural activities neglected in conventional neural network architectures. Implementing this extension into a simple self-organizing network based on a feature map, we present an associative memory that actually is capable of forming hypotheses of classification.

Animals↗

PubMed1992

Application of neural networks in medical diagnosis: the case of sexually-transmitted diseases.

In this paper neural networks are used to formulate a means of diagnosing sexually-transmitted diseases (STD's). An overview of the theoretical background of neural networks is presented. A summary of the diseases used in the project and their signs and symptoms are given. These signs and symptoms are used to train a network. Upon presentation of a set of signs and symptoms to the trained network, the presence of a disease or diseases with those symptoms may be deduced.

Diagnosis, Computer-Assisted↗

PubMed1999

Approximation techniques for neuromimetic calculus.

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.

Algorithms↗

PubMed1997

Current keratoconus detection methods compared with a neural network approach.

PURPOSE: Four videokeratographic methods for keratoconus detection were compared with a neural network approach. METHODS: A classification neural network for keratoconus screening was designed to detect the presence of keratoconus (KC) or keratoconus suspects (KCS); a separate cone severity network graded the severity of conelike topography patterns consistent with KC or KCS. Three hundred TMS-1 examinations (Tomey) were randomly divided into training and test sets. Ten topographic indexes were network inputs. Nine categories were used: normal, astigmatism, KC, KCS, contact lens-induced warpage, pellucid marginal degeneration, photorefractive keratectomy, radial keratotomy, and penetrating keratoplasty. KC was subdivided into KC1 (mild), KC2 (moderate), and KC3 (advanced). There were three outputs for the classification network (KC, KCS, and OTHER); target output values of 0 = OTHER, 0.25 = KCS, 0.5 = KC1, 0.75 = KC2, and 1.0 = KC3 were used for the severity network. RESULTS: The best-trained classification network had 100% accuracy, specificity, and sensitivity for the test set. The severity network had mean outputs (+/-standard deviation) of OTHER = 0.02+/-0.02, KCS = 0.21+/-0.05, KC1 = 0.52+/-0.17, KC2 = 0.74+/-0.12, and KC3 = 0.91+/-0.15. The severity network output for all categories was well correlated to the keratoconus prediction index (R = 0.892, P < 0.0001). The classification network had an overall accuracy and specificity significantly better (P < or = 0.005) than the Klyce/Maeda keratoconus index (KCI) test, the Rabinowitz test (K & I-S), and simulated keratometry (average Sim K). However, there were no significant differences in keratoconus sensitivity between the classification network, KCI, and K & I-S. The sensitivity and specificity of average Sim K were significantly worse than those of the other tests. The classification network had significantly better sensitivity (P < 0.001) and specificity (P = 0.025) for KCS detection than the K & I-S. CONCLUSIONS: The neural networks completely distinguished KC from KCS and from topographies that resembled KC. The network approach equaled the sensitivity of currently used tests for keratoconus detection and outperformed them in terms of accuracy and specificity.

Cornea↗

PubMed1995

Identification of a hypoperfused segment in bull's-eye myocardial perfusion images using a feed forward neural network.

Artificial neural networks are computer systems which can be trained to recognize similarities in patterns and which learn by example; one of the more straightforward types being the feed forward neural network (FFNN). We previously reported the use of FFNNs for classification of hypoperfusion patterns in bull's-eye representation of 201Tl single photon emission tomography myocardial perfusion studies and showed that, when such an image was divided into 24 segments, FFNNs could detect perfusion defects without direct comparison to a normal data base. This has been extended in this investigation to assess the ability of an FFNN, trained on data in which only a single segment was hypoperfused, to detect this abnormal segment when the hypoperfusion pattern of the other segments in the image varied. The results indicated that the network could reliably determine whether a segment was normally or under perfused, with accuracies of 99% and 100%, respectively, if all other segments were normally perfused. It could also reliably detect a normally perfused segment, even if other segments were hypoperfused, with accuracies of 95% and 98%. The network was less reliable, however, in detecting a hypoperfused segment when other segments were also hypoperfused, showing accuracies of only 74% and 88%.

Blood Flow Velocity↗

PubMedDate not supplied

Prognostic markers in localized prostate cancer: from microscopes to molecules.

Management of patients diagnosed with localized prostate cancer is complicated by the diverse natural history of the disease and variable response to treatment. Prognostic criteria currently in use cannot fully predict tumor behavior and thus limit the ability to recommend treatment regimens with the assurance that they are the best course of action for each individual patient. The search for better prognostic markers is now focussed on the molecular mechanisms which underlay tumor behavior, such as altered cell cycle progression, apoptosis, neuroendocrine differentiation, and angiogenesis. As the number of potential molecular markers increases, it is becoming evident that no single marker will provide the prognostic information necessary to make a significant improvement in patient care. In addition, it seems likely that traditional methods of assessing the prognostic value of this multitude of new markers will prove inadequate. In this review, we briefly examine the current state of prognostication in localized prostate cancer and some of the promising new molecular markers. Next, we examine how new technologies may allow the multiplex analysis of vast numbers of markers and how computational methods such as artificial neural networks will provide meaningful interpretation of the data. In the near future, such an integrated approach may provide a comprehensive prognostic tool for localized prostate cancer.

Antigens, Surface↗