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At least 127 records · Page 7Linked to original sources
PubMed1999

Artificial neural network analysis of common femoral artery Doppler shift signals: classification of proximal disease.

The aim of this study was to apply artificial neural networks (ANNs) to the problem of the diagnosis of aorto-iliac arterial disease on the basis of the profile of the common femoral artery (CFA) Doppler flow velocity waveform. The maximum frequency envelopes obtained from the CFA of 180 subjects were used to create sets of training and testing vectors for a back-propagation ANN. The ANN had three outputs: one representing the absence of significant aorto-iliac disease (i.e., < 50% diameter stenosis), one representing the presence of a hemodynamically significant aorto-iliac stenosis (i.e., 50-99% stenosis), and the other representing the presence of an aorto-iliac occlusion. After training, the ANN correctly classified 80% of "no significant disease" testing data, 45% of "significant stenosis" data and 85% of "occlusion" data. This work, thus, demonstrated the ability of an ANN to identify the severity of aorto-iliac disease from the CFA waveform. Although the ANN outperformed standard univariate methods and visual classification of the data, it would appear that further work is needed to increase the accuracy of the ANN to a clinically acceptable standard.

Adult↗
PubMed2002

Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis.

The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.

Journal Article↗
PubMed2000

Neural network analysis of clinicopathological and molecular markers in bladder cancer.

PURPOSE: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. MATERIALS AND METHODS: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. RESULTS: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). CONCLUSIONS: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.

Biomarkers, Tumor↗
PubMed2001

Neuronal network analysis based on arrival times of active-sleep specific inhibitory postsynaptic potentials in spinal cord motoneurons of the cat.

The neuronal network responsible for motoneuron inhibition and loss of muscle tone during active (REM) sleep can be activated by the injection of the cholinergic agonist carbachol into a circumscribed region of the brainstem reticular formation. In the present report, we studied the arrival times of inhibitory postsynaptic potentials (IPSPs) observed in intracellular recordings from cat spinal cord motoneurons. These recordings were obtained during episodes of motor inhibition induced by carbachol or during motor inhibition associated with naturally occurring active sleep. When the observed IPSP arrival times were analyzed as a superposition of renewal processes occurring in a pool of pre-motor inhibitory interneurons, it was possible to estimate the following parameters: (1) the number of independent sources of the IPSPs; (2) the rate at which each source was bombarded with excitatory postsynaptic potentials (EPSPs); and (3) the number of EPSPs required to bring each source to threshold. From the data based upon the preceding parameters and the unusually large amplitudes of the active sleep-specific IPSPs, we suggest that each source is a cluster of synchronously discharging pre-motor inhibitory interneurons. The analysis of IPSP arrival times as a superposition of renewal processes, therefore, provides quantitative information regarding neuronal activity that is as far as two synapses upstream from the site of the recording electrode. Consequently, we suggest that a study of the temporal evolution of these parameters could provide a basis for dynamic analyses of this neuronal network and, in the future, for other neuronal networks as well.

Animals↗
PubMedDate not supplied

Neural network analysis of breast cancer from MRI findings.

PURPOSE: To evaluate how much the experience of radiologists affects the performance of an artificial neural network (ANN) trained by two highly experienced radiologists. MATERIALS AND METHODS: Before biopsy two experienced radiologists reviewed the MR images of 100 adult patients with suspicious breast lesions and evaluated their findings based on six features. This database was then used to train a three-layered feed-forward neural network. The network's generalizing ability was then tested to predict the outcome of biopsy in 56 new patients' records which were extracted by 10 participating radiologists. The MRI findings of each reader were presented to the ANN to evaluate the effect of various levels of experience on the output of the ANN. The performance of the ANN was then compared with that of attendant physicians in terms of sensitivity, specificity, and accuracy as well as ROC analysis. RESULTS: The best ANN outcome offered a correct diagnosis in 40 of 41 of the patients with malignant breast cancer and 10 of 15 with benign entity presented in the testing set. The output of the trained ANN outperformed the attendant radiologists with low levels of experience and showed comparable performance with radiologists with higher levels of experience. CONCLUSIONS: The ANN is able to work as a backup system to assist radiologists in the diagnosis of breast cancer.

Adolescent↗
PubMed1998

Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: a pilot study.

Prostate cancer is the second most common malignancy in men in the UK. The disease is unpredictable in its behaviour and, at present, no single investigative method allows clinicians to differentiate between tumours that will progress and those that will remain quiescent. There is an increasing need for novel means to predict prognosis and outcome of the disease. The aim of this study was to assess the value of artificial neural networks in predicting outcome in prostate cancer in comparison with statistical methods, using a combination of conventional and experimental biological markers. Forty-one patients with different stages and grades of prostate cancer undergoing a variety of treatments were analysed. Artificial neural networks were used as follows: eight input neurons consisting of six conventional factors (age, stage, bone scan findings, grade, serum PSA, treatment) and two experimental markers (immunostaining for bcl-2 and p53, which are both apoptosis-regulating genes). Twenty-one patients were used for training and 20 for testing. A total of 80% of the patients were correctly classified regarding outcome using the combination of factors. When both bcl-2 and p53 immunoreactivity were excluded from the analysis, correct prediction of the outcome was achieved in only 60% of the patients (P = 0.0032). This study was able to demonstrate the value of artificial neural networks in the analysis of prognostic markers in prostate cancer. In addition, the potential for using this technology to evaluate novel markers is highlighted. Further large-scale analyses are required to incorporate this methodology into routine clinical practice.

Aged↗
PubMed2002

Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis.

Diabetes is a major health problem in both industrial and developing countries, and its incidence is rising. Although detection of diabetes is improving, about half of the patients with Type 2 diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 yr. Thus, earlier detection of Type 2 diabetes and treatment of hyperglycaemia and related metabolic abnormalities is of vital importance. The objectives of the present study were to examine urine samples from Type 2 diabetic patients and healthy volunteers using the electronic nose technology and to evaluate possible application of data classification methods such as self-learning artificial neural networks (ANN) and logistic regression (LR) in comparison with principal components analysis (PCA). Urine samples from Type 2 diabetic patients and healthy controls were processed randomly using a simple 8-sensors electronic nose and individual electronic nose patterns were qualitatively classified using the "Approximation and Classification of Medical Data" (ACMD) network based on 2 output neurons, binary LR analysis and PCA. Distinct classes were found for Type 2 diabetic subjects and controls using PCA, which had a 96.0% successful classification percentage mean while qualitative ANN analysis and LR analysis had successful classification percentages of 92.0% and 88.0%, respectively. Therefore, the ACMD network is suitable for classifying medical and clinical data.

Aged↗
PubMed2001

Neural network analysis of the prediction of cancer recurrence following debulking laparotomy and chemotherapy in stages III and IV ovarian cancer.

An artificial neural network (ANN) has been developed to predict the presence or absence of cancer following debulking laparotomy and chemotherapy in patients with stages III and IV ovarian cancer. The presence or absence of a residual gross tumor or microscopic disease was determined by a second-look laparotomy. The ANN was trained and tested using detailed operative findings and related surgical procedures associated with the debulking surgery. The ANN predictive results were compared with linear and logistic regression. The ANN significantly outperformed both logistic and linear regression analyses, but additional cases are needed to validate the network.

Female↗
PubMed2001

Neural-network analysis of the vibrational spectra of N-acetyl L-alanyl N'-methyl amide conformational states.

Density-functional theory (DFT) calculations utilizing the Becke 3LYP hybrid functional have been carried out for N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA), vibrational circular dichroism (VCD), Raman spectra, and Raman optical activity (ROA) intensities. The large changes due to hydration in the structures, and the relative stability of the conformer, reflected in the VA, VCD, Raman spectra, and ROA spectra observed experimentally, are reproduced by the DFT calculations. A neural network has been constructed for reproducing the inverse scattering data (we infer the structural coordinates from spectroscopic data) that the DFT method could produce. The purpose of the network has also been to generate the large set of conformational states associated with each set of spectroscopic data for a given conformer of the molecule by interpolation. Finally the neural network performances are used to monitor a sensitivity analysis of the importance of secondary structures and the influence of the solvent. The neural network is shown to be good in distinguishing the different conformers of the small alanine peptide, especially in the gas phase.

Alanine↗
PubMed2002

Increasing social capital via local networks: analysis in the context of a surgical practice.

The relationship between social capital (support, trust, patient awareness, and increased practice revenue) and local networks (university hospital) in communities has received little attention. The development of computer-based communication networks (social networks) has added a new dimension to the argument, posing the question of whether local networks can (re-)create social capital in local communities. This relationship is examined through a review of the literature on local networks and social capital and a surgeon's practice management from 1990 to 2001 with respect to repair of pectus chest deformities. With respect to pectus repair there was a consistent but small number of new referrals (15-20 new patients/year), lack of patient awareness (eight to 12 self-referred patients/year), and modest practice revenue. Since the inception of an Internet website (social network) dedicated to pectus repair in 1996 there has been increased social participation (n = 630 hits/year to the website); facilitation of spread of information through E-mail messages (n = 430 messages/year); and a greater participation of groups such as women, minorities, adults, and those with disability (n = 120 patients/year). The dissemination of information via the local network has also allowed an "outward movement" with increased participation by interconnecting communities (n = 698,300 global Internet participants based on statistical ratios). We conclude that local networks have enhanced social networks providing new grounds for the development of relationships based on choice and shared interest.

Adult↗
PubMed1999

Neural network analysis of preoperative variables and outcome in epilepsy surgery.

OBJECT: Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data has been successful in predicting medical outcome. METHODS: The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom data on demographic, seizure, operative, and clinical variables to predict postoperative seizures were collected. Neural networks could be used to predict postoperative seizures in up to 98% of cases. Student's t-tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures: using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathological category (98%); all data excluding pathological category, intelligence quotient (IQ) data, and Wada results (84%); only demographics and tissue pathological category (65%); and only IQ data (63%). CONCLUSIONS: Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.

Anticonvulsants↗
PubMed2003

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation.

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.

Ammonia↗
PubMed2002

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound.

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15-50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91%, respectively.

Animals↗
PubMed2026

Examining early-phase symptom trajectories in interpersonal psychotherapy versus antidepressant medication for adults with depression: A dynamic time warp network analysis.

BACKGROUND: Depression is characterized by substantial symptom heterogeneity, which is often concealed when examining total severity scores. Analyzing symptom-level change can improve our understanding of treatment effects and recovery processes. This study, therefore, examined dynamic symptom networks during early-phase interpersonal psychotherapy (IPT) and selective serotonin reuptake inhibitor (SSRI) antidepressant treatment, assessing patterns of symptom change across as well as differences between treatments. METHODS: Using weekly item-level Hamilton Depression Rating Scale (HAM-D) data from a randomized clinical trial comparing IPT and SSRIs for adults with depression, this preregistered study examined symptom trajectories in the first six weeks of treatment with Dynamic Time Warping (DTW). RESULTS: Depressive symptom trajectories and DTW-based symptom networks were largely similar for IPT and SSRI. In both conditions, changes in somatic symptoms of anxiety and middle insomnia tended to precede improvements in depressed mood. CONCLUSIONS: Early symptom change may occur outside the core affective domain, underscoring the importance of monitoring symptoms broadly. Symptom-level patterns may reflect patients' stage of recovery and provide clinically relevant information beyond total severity scores. The absence of differences in improvement patterns between IPT and SSRI suggest few indications for treatment selection based on baseline symptom profiles. Future research should replicate and extend these findings to subsequent treatment phases using more frequent assessments and a broader range of interventions.

Humans↗
PubMed2002

Quantification of human brain metabolites from in vivo 1H NMR magnitude spectra using automated artificial neural network analysis.

Long echo time (TE=270 ms) in vivo proton NMR spectra resembling human brain metabolite patterns were simulated for lineshape fitting (LF) and quantitative artificial neural network (ANN) analyses. A set of experimental in vivo 1H NMR spectra were first analyzed by the LF method to match the signal-to-noise ratios and linewidths of simulated spectra to those in the experimental data. The performance of constructed ANNs was compared for the peak area determinations of choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) signals using both manually phase-corrected and magnitude spectra as inputs. The peak area data from ANN and LF analyses for simulated spectra yielded high correlation coefficients demonstrating that the peak areas quantified with ANN gave similar results as LF analysis. Thus, a fully automated ANN method based on magnitude spectra has demonstrated potential for quantification of in vivo metabolites from long echo time spectroscopic imaging.

Aspartic Acid↗
PubMed1996

Introduction of a new laboratory test: an econometric approach with the use of neural network analysis.

We designed a simulation model for the assessment of the financial risks involved when a new diagnostic test is introduced in the laboratory. The model is based on a neural network consisting of ten neurons and assumes that input entities can have assigned appropriate uncertainty. Simulations are done on a 1-day interval basis. Risk analysis completes the model and the financial effects are evaluated for a selected time period. The basic output of the simulation consists of total expenses and income during the simulation time, net present value of the project at the end of simulation, total number of control samples during simulation, total number of patients evaluated and total number of used kits.

Chemistry, Clinical↗