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Results for “Pattern Recognition, Automated”

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PubMed1979

Differentiating exogenous psychiatric illness from schizophrenia.

Retaining an individual on psychiatric dispensary lists long after a single psychotic episode can result in unnecessary restriction of his or her social-vocational rights and responsibilities. This study demonstrates that an early clinical differention can be made between exogenous psychoses and progressive schizophrenia. The validity of the clinical differentiation was enhanced by demonstrating that a computer learning and pattern-recognition program was capable of using signs and symptoms recorded in the first psychotic episode to make a differential diagnosis that coincided closely with diagnoses made at a later date by clinicians aware of the subsequent clinical course. This kind of approach to standardized nosologic principles may expand the possibility for more appropriate application of psychotropic medications, psychotherapy, and somatic treatments, as well as more accurate social-vocational prognoses.

Acute Disease↗
PubMed1987

Use of pattern analysis to identify true drug response. A replication.

In any antidepressant study, placebo response in patients assigned active drug is a troubling source of variance. There have been few attempts to identify the patients whose conditions improve as a result of true drug effect, in contrast with improvement that is a result of nonspecific effects. In a previous report we demonstrated that true drug effect seemed to be characterized by a two-week delay in onset and persistence. We described a method of pattern analysis to identify such patients. In this report, we describe the use of pattern analysis to replicate our initial findings. Data from a new sample of 150 nonmelancholic patients support the hypothesis that true drug effect is characterized by a two-week delay in onset and persistence of improvement, once achieved. There was little evidence of the onset of antidepressant effect before two weeks. The theoretical and clinical implications of this work are discussed.

Actuarial Analysis↗
PubMed1998

Use of pattern analysis to predict differential relapse of remitted patients with major depression during 1 year of treatment with fluoxetine or placebo.

BACKGROUND: Delayed and persistent ("true drug") improvement characterizes the response to antidepressant medication. Early or nonpersistent ("placebo") benefit is typical of a placebo response. The prediction was that patients with a true drug response would sustain their benefit best if they continued to receive the drug and that patients with a placebo response would have an equivalent prognosis whether they continued to receive the drug or were switched to placebo. METHODS: Patients with major depression who met the study's response criteria (a modified Hamilton Depression Rating Scale score < or =7 and failure to meet major depression criteria after each of the last 3 weeks following 12 to 14 weeks of treatment with fluoxetine hydrochloride, 20 mg/d) were enrolled in a 50-week randomized placebo substitution trial during which the return of depressive symptoms defined relapse. The timing and persistency of response during initial treatment defined true drug or placebo response patterns. RESULTS: Patients with a true drug response pattern relapsed significantly more frequently if they were switched to placebo than if they continued to receive fluoxetine (P<.001 for weeks 12-26, P<.005 for weeks 26-50, and P<.41 for weeks 50-62). Patients with a placebo response pattern had an equivalent outcome whether maintained on fluoxetine therapy or placebo (P< .20 for weeks 12-26, test invalid for weeks 26-50, and P<.67 for weeks 50-62). Patients with a placebo response pattern relapsed more often when they continued to receive fluoxetine than patients with a true drug response pattern (P<.01 for weeks 12-26, P<.10 for weeks 26-50, and P<.36 for weeks 50-62). CONCLUSIONS: These findings confirm that pattern analysis validly differentiates true drug from nonspecific initial responses and extend its use to the continuation and maintenance phases of treatment for depression. Investigations into the mechanisms of antidepressant activity might best be limited to those that can account for delayed efficacy. Fluoxetine's efficacy during the continuation and maintenance phases of treatment may be limited to patients with a true drug pattern of initial response.

Adolescent↗
PubMed1998

Energy minimization studies on alpha-turns.

Using a grid search technique, the entire conformational space of a system of four linked peptide units (tetrapeptide) was scanned to pick out geometrically possible 5-->1 type hydrogen-bonded conformations defined as an alpha-turn. The energy minimization of these conformations led to 23 distinct minimum energy conformations (MECs) falling in 13 different classes. The presence of beta and gamma turn type hydrogen bonds along with 5-->1 type hydrogen bond gave conformational variability in a given class. The occurrence of bifurcated hydrogen bonding network was a characteristic feature of most of the MECs. In many prototype MECs non-glycyl residues such as Ala and Pro could be accommodated. Comparison of MECs with the alpha-turn examples that are observed in proteins showed that the conformationally worked out MECs occurred in isolation in proteins, with the alpha-helical alpha-turn being distinctly the most predominant.

Energy Metabolism↗
PubMed1997

Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review.

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in magnetic resonance spectroscopy. It may be argued that such techniques will facilitate putting MRS technology to practical clinical use. This paper reviews approaches of pattern recognition commonly used in the analysis of MR spectra for biomedical applications. It briefly introduces the mathematical and algorithmic formulation of each of the techniques, noting their developmental background and their relationship to each other, and discusses their strengths and limitations. It then reviews how these techniques have been implemented in MRS applications. In doing so the paper also highlights a number of problems related to the design and testing of MRS/pattern recognition applications which currently prevent these techniques from being in wide practical clinical use, and suggests ways to avoid those pitfalls.

Humans↗
PubMedDate not supplied

From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods.

This article reviews the wealth of different pattern recognition methods that have been used for magnetic resonance spectroscopy (MRS) based tumor classification. The methods have in common that the entire MR spectra is used to develop linear and non-linear classifiers. The following issues are addressed: (i) pre-processing, such as normalization and digitization, (ii) extraction of relevant spectral features by multivariate methods, such as principal component analysis, linear discriminant analysis (LDA), and optimal discriminant vector, and (iii) classification by LDA, cluster analysis and artificial neural networks. Different approaches are compared and discussed in view of practical and theoretical considerations.

Humans↗
PubMedDate not supplied

Magnetic resonance image segmentation using pattern recognition, and applied to image registration and quantitation.

This review highlights various magnetic resonance image (MRI) segmentation algorithms that employ pattern recognition. The procedures are grouped into two categories: low- to intermediate-level, and high-level image processing. The former consists of grey level histogram analysis, texture definition, edge identification, region growing, and contour following. The roles of significant prior knowledge, neural networks and cluster analysis are examined by producing objective identification of anatomical structures. The application of the segmented anatomical structures in image registration, to monitor the disease progression or growth of anatomy in normal volunteers and patients, is highlighted. The use of the segmented anatomy in measuring volumes of structures in normals and patients is also examined.

Humans↗
PubMedDate not supplied

Using pattern analysis of in vivo proton MRSI data to improve the diagnosis and surgical management of patients with brain tumors.

We have used pattern analysis of proton magnetic resonance spectroscopic imaging (1H MRSI) data in a variety of situations related to the clinical management of patients with brain tumors and other cerebral space-occupying lesions (SOLs). Here, we review how 'leave-one-out' linear discriminant analyses (LDAs) of in vivo 1H MRSI spectral patterns have enabled us to quickly, accurately, and non-invasively: (1) discriminate amongst tissue arising from the five most common types of supratentorial tumors found in adults, and (2) use the metabolic heterogeneity of cerebral SOLs to predict certain pathological characteristics that are useful in guiding stereotaxic biopsy and selective tumor resection. These findings suggest that pattern analysis of 1H MRSI data can significantly improve the diagnostic specificity and surgical management of patients with certain cerebral SOLs.

Aged↗
PubMedDate not supplied

Brain tumor segmentation and characterization by pattern analysis of multispectral NMR images.

A major problem in tumor treatment planning and evaluation is determination of the tumor extent. This paper presents a pattern analysis methodology for segmentation and characterization of brain tumors from multispectral NMR images. The proposed approach has been used in 15 clinical studies of cerebral tumor patients who have been scheduled for surgical biopsy and resection. The tissue biopsy results, obtained at specific spatial coordinates determined in the analysis, have been utilized to validate the methodology. It was found that in all cases the lesion had extended into normal tissue, at least to the location where the sample was taken. In most cases, the proposed method suggested that the lesion had extended several millimetres beyond the point from where the biopsy sample was taken. In some cases, the extent of the lesion into normal tissue was well beyond the boundary seen on T1- or T2-weighted images. It is concluded that the proposed approach indicates brain tumor infiltration more precisely than what is visualized in the original NMR images and therefore its utilization facilitates proper treatment planning for the cerebral tumor patients.

Biopsy↗
PubMedDate not supplied

Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection.

Magnetic resonance spectroscopy opens a window into the biochemistry of living tissue. However, spectra acquired from different tissue types in vivo or in vitro and from body fluids contain a large number of peaks from a range of metabolites, whose relative intensities vary substantially and in complicated ways even between successive samples from the same category. The realization of the full clinical potential of NMR spectroscopy relies, in part, on our ability to interpret and quantify the role of individual metabolites in characterizing specific tissue and tissue conditions. This paper addresses the problem of tissue classification by analysing NMR spectra using statistical and neural network methods. It assesses the performance of classification models from a range of statistical methods and compares them with the performance of artificial neural network models. The paper also assesses the consistency of the models in selecting, directly from the spectra, the subsets of metabolites most relevant for differentiating between tissue types. The analysis techniques are examined using in vitro spectra from eight classes of normal tissue and tumours obtained from rats. We show that, for the given data set, the performance of linear and non-linear methods is comparable, possibly due to the small sample size per class. We also show that using a subset of metabolites selected by linear discriminant analysis for further analysis by neural networks improves the classification accuracy, and reduces the number of metabolites necessary for correct classification.

Animals↗
PubMedDate not supplied

Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition.

Pattern recognition approaches were developed and applied to the classification of 600 MHz 1H NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in either the liver or kidney. Male rats were separated into groups (n = 5) and each treated with one of the following compounds; adriamycin, allyl alcohol, 2-bromoethanamine hydrobromide, hexachlorobutadiene, hydrazine, lead acetate, mercury II chloride, puromycin aminonucleoside, sodium chromate, thioacetamide, 1,1,2-trichloro-3,3,3-trifluoro-1-propene or dose vehicle. Urine samples were collected over a 7 day time-course and analysed using 600 MHz 1H NMR spectroscopy. Each NMR spectrum was data-reduced to provide 256 intensity-related descriptors of the spectra. Data corresponding to the periods 8-24 h, 24-32 h and 32-56 h post-dose were first analysed using principal components analysis (PCA). In addition, samples obtained 120-144 h following the administration of adriamycin and puromycin were included in the analysis in order to compensate for the late onset of glomerular toxicity. Having established that toxin-related clustering behaviour could be detected in the first three principal components (PCs), three-quarters of the data were used to construct a soft independent modelling of class analogy (SIMCA) model. The remainder of the data were used as a test set of the model. Only three out of 61 samples in the test set were misclassified. Finally as a further test of the model, data from the 1H NMR spectra of urine from rats that had been treated with uranyl nitrate were used. Successful prediction of the toxicity type of the compound was achieved based on NMR urinalysis data confirming the robust nature of the derived model.

Animals↗
PubMed1987

Applications of the pattern variability index (sigma z) to the quantification of dysmorphogenesis in the hand.

We have generated percentiles for the pattern variability index (sigma z) of the hand in 1,088 normal infants, children, and adults and have analyzed pattern variability indices for 820 individuals representing 50 congenital malformation syndromes with respect to the normal percentiles. The majority of the affected individuals exhibited elevated sigma z values for the hand, some vastly in excess of normal, while such syndromes as Down, Turner, and the Prader-Willi syndrome were low rather than high in pattern variability of the hand.

Adult↗