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PubMed2003

A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index.

BACKGROUND: The SF-6D is a new health state classification and utility scoring system based on 6 dimensions ('6D') of the Short Form 36, and permits a "bridging" transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3. METHODS: Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC). RESULTS: SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60-0.63) and 0.58 (95% CI 0.54-0.62) respectively; a difference of 0.03 (p<0.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30-0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48-0.68) and agreement by ICC was 0.42 (95% CI 0.31-0.52). Correlations between dimensions of SF-6D were higher than for HUI3. CONCLUSIONS: Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities.

Algorithms↗

PubMed2005

Ejaculatory disorders: epidemiology and current approaches to definition, classification and subtyping.

Ejaculatory disorders (disorders of emission, ejaculation and orgasm) are the most frequent sexual complaint. Conventional algorithms on ejaculatory disorders are based on an organic or psychogenic dichotomy, with the latter being traditionally considered the main etiological cause. The scope of this review is to propose a new classification of ejaculatory disorders, with special emphasis on epidemiology and subtyping of the most frequent: premature ejaculation (PE). There is growing agreement on definition, diagnosis, and treatment options. In many cases, ejaculatory disorders can be classified by psycho-neuro-uro-endocrine symptoms. Epidemiological data, new classifications and subtyping, together with new diagnostic procedures and the availability of pharmacological aids, place this topic in the mainframe of sexual medicine. This will soon demolish the barriers to seeking help perceived by patients suffering ejaculatory disorders.

Ejaculation↗

PubMedDate not supplied

Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment.

The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.

Algorithms↗

PubMed2003

Normalization of brain magnetic resonance images using histogram even-order derivative analysis.

The even-ordered (2nd, 4th and 6th) derivatives of a brain MRI histogram were used to calculate a characteristic value for white matter, which was used to normalize the image intensity scale. Simulated image histograms were used to estimate the methodological error as a function of noise level, and the optimum derivative order was determined for each image type studied (T1-, T2- and density-weighted). The algorithm yielded highly reproducible results when used in conjunction with a threshold-sensitive brain segmentation algorithm. It also proved insensitive to the presence of extra-cranial tissues. This method of histogram analysis could find utility in a variety of applications that demand robust intensity normalization including image registration, brain segmentation, tissue classification and spatial inhomogeneity correction.

Algorithms↗

PubMed2006

Terminated Ramp-Support vector machines: a nonparametric data dependent kernel.

We propose a novel algorithm, Terminated Ramp-Support Vector Machines (TR-SVM), for classification and feature ranking purposes in the family of Support Vector Machines. The main improvement relies on the fact that the kernel is automatically determined by the training examples. It is built as a function of simple classifiers, generalized terminated ramp functions, obtained by separating oppositely labeled pairs of training points. The algorithm has a meaningful geometrical interpretation, and it is derived in the framework of Tikhonov regularization theory. Its unique free parameter is the regularization one, representing a trade-off between empirical error and solution complexity. Employing the equivalence between the proposed algorithm and two-layer networks, a theoretical bound on the generalization error is also derived, together with Vapnik-Chervonenkis dimension. Performances are tested on a number of synthetic and real data sets.

Algorithms↗

PubMed2006

Fuzzy classification by fuzzy labeled neural gas.

We extend the neural gas for supervised fuzzy classification. In this way we are able to learn crisp as well as fuzzy clustering, given labeled data. Based on the neural gas cost function, we propose three different ways to incorporate the additional class information into the learning algorithm. We demonstrate the effect on the location of the prototypes and the classification accuracy. Further, we show that relevance learning can be easily included.

Algorithms↗

PubMed2004

SED, a normalization free method for DNA microarray data analysis.

BACKGROUND: Analysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other. Both simple total array intensity-based as well as more complex "local intensity level" dependent normalization methods have been developed, some of which are widely used. Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors. RESULTS: Instead of focusing on the raw intensity levels, we developed a new method for microarray data analysis that maps each gene's expression intensity level to a high dimensional space of SEDs (Signs of Expression Difference), the signs of the expression intensity difference between a given gene and every other gene on the array. Since SED are unchanged under any monotonic transformation of intensity levels, the SED based method is normalization free. When tested on a multi-class tumor classification problem, simple Naive Bayes and Nearest Neighbor methods using the SED approach gave results comparable with normalized intensity-based algorithms. Furthermore, a high percentage of classifiers based on a single gene's SED gave good classification results, suggesting that SED does capture essential information from the intensity levels. CONCLUSION: The results of testing this new method on multi-class tumor classification problems suggests that the SED-based, normalization-free method of microarray data analysis is feasible and promising.

Central Nervous System Neoplasms↗

PubMed1999

[The concept and use of artificial neural networks in medicine].

The use of neural networks in medicine is concentrated mainly on classification purposes. In particular, neural networks applications in spectroscopy are discussed, where this approach offers powerful algorithmic tools for interpretation of spectral data and elucidation of chemical structure of compounds. Neural networks are effective also for the classification and prediction of chemical reactivity and structure of proteins and also for QSAR and QSRR studies. At present the most successful use of neural networks in clinical medicine is image analysis and analysis of wave forms--ECG or EEG pattern recognition and classification and partly also clinical diagnosis and prognosis. (Tab. 7, Fig. 2, Ref. 170.)

Neural Networks, Computer↗

PubMed2000

Relationship between ultrasound texture classification images and histology of atherosclerotic plaque.

Structure and content of atherosclerotic plaque varies between patients and may be indicative of their risk for embolisation. This study aimed to construct parametric images of B-scan texture and assess their potential for predicting plaque morphology. Sequential transverse in vitro scans of 10 carotid plaques, excised during endarterectomy, were compared with macrohistology maps of plaque content. Multidiscriminant analysis combined the output of 157 statistical and textural algorithms into five separate texture classes, displayed as ultrasound (US) texture classification images (UTCI). Visual comparison between corresponding UTCI and histology maps found the five texture classes matched with the location of fibrin, elastin, calcium, haemorrhage or lipid. However, histology preparation removes calcium and lipid and, so, can affect the structural integrity of atherosclerotic plaques. Soft tissue regions smaller than the UTCI kernel, (0.87 mm x 0.85 mm x 3.9 mm), such as blood clots, are also difficult to detect by UTCI. These factors demonstrate limitations in the use of histology as a "gold standard" for US tissue characterisation.

Arteriosclerosis↗

PubMed2005

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database.

A computer algorithm for nodule detection in chest radiographs is presented. The algorithm consists of four main steps: (i) image preprocessing; (ii) nodule candidate detection; (iii) feature extraction; (iv) candidate classification. Two optional extensions to this scheme are tested: candidate selection and candidate segmentation. The output of step (ii) is a list of circles, which can be transformed into more detailed contours by the extra candidate segmentation step. In addition, the candidate selection step (which is a classification step using a small number of features) can be used to reduce the list of nodule candidates before step (iii). The algorithm uses multi-scale techniques in several stages of the scheme: Candidates are found by looking for local intensity maxima in Gaussian scale space; nodule boundaries are detected by tracing edge points found at large scales down to pixel scale; some of the features used for classification are taken from a multi-scale Gaussian filterbank. Experiments with this scheme (with and without the segmentation and selection steps) are carried out on a previously characterized, publicly available database, that contains a large number of very subtle nodules. For this database, counting as detections only those nodules that were indicated with a confidence level of 50% or more, radiologists previously detected 70% of the nodules. For our algorithm, it turns out that the selection step does have an added value for the system, while segmentation does not lead to a clear improvement. With the scheme with the best performance, accepting on average two false positives per image results in the identification of 51% of all nodules. For four false positives, this increases to 67%. This is close to the previously reported 70% detection rate of the radiologists.

Algorithms↗

PubMed2005

Tissue classification of noisy MR brain images using constrained GMM.

We present an automated algorithm for tissue segmentation of noisy, low contrast magnetic resonance (MR) images of the brain. We use a mixture model composed of a large number of Gaussians, with each brain tissue represented by a large number of the Gaussian components in order to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through parameter tying of all the related Gaussians. The EM algorithm is utilized to learn the parameter-tied Gaussian mixture model. A new initialization method is applied to guarantee the convergence of the EM algorithm to the global maximum likelihood. Segmentation of the brain image is achieved by the affiliation of each voxel to a selected tissue class. The presented algorithm is used to segment 3D, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Quantitative results are presented and compared with state-of-the-art results reported in the literature.

Algorithms↗

PubMed2006

The many faces of protein-protein interactions: A compendium of interface geometry.

A systematic classification of protein-protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces) for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at http://www.scoppi.org.

Amino Acid Sequence↗

PubMed2004

Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions.

BACKGROUND: Digital image analysis has been introduced into the diagnosis of skin lesions based on dermoscopic pictures. OBJECTIVES: To develop a computer algorithm for the diagnosis of melanocytic lesions and to compare its diagnostic accuracy with the results of established dermoscopic classification rules. METHODS: In the Department of Dermatology, University of Tuebingen, Germany, 837 melanocytic skin lesions were prospectively imaged by a dermoscopy video system in consecutive patients. Of these lesions, 269 were excised and examined by histopathology: 84 were classified as cutaneous melanomas and 185 as benign melanocytic naevi. The remaining 568 lesions were diagnosed by dermoscopy as benign. Digital image analysis was performed in all 837 benign and malignant melanocytic lesions using 64 different analytical parameters. RESULTS: For lesions imaged completely (diameter < or = 12 mm), three analytical parameters were found to distinguish clearly between benign and malignant lesions, while in incompletely imaged lesions six parameters enabled differentiation. Based on the respective parameters and logistic regression analysis, a diagnostic computer algorithm for melanocytic lesions was developed. Its diagnostic accuracy was 82% for completely imaged and 84% for partially imaged lesions. All 837 melanocytic lesions were classified by established dermoscopic algorithms and the diagnostic accuracy was found to be in the same range (ABCD rule 78%, Menzies' score 83%, seven-point checklist 88%, and seven features for melanoma 81%). CONCLUSIONS: A diagnostic algorithm for digital image analysis of melanocytic lesions can achieve the same range of diagnostic accuracy as the application of dermoscopic classification rules by experts. The present diagnostic algorithm, however, still requires a medical expert who is qualified to recognize cutaneous lesions as being of melanocytic origin.

Algorithms↗

PubMed2000

EEG-based discrimination between imagination of left and right hand movements using Adaptive Gaussian Representation.

This article uses the Adaptive Gaussian Representation (AGR) for human electroencephalogram (EEG) feature extraction aiming the discrimination among mental tasks to be used in a brain computer interface (BCI). It does not focus on the AGR time-frequency representation, but rather on their projection coefficients. Ten volunteers were asked to imagine either right or left hand movement, according to a proper visual stimulus. The features of the resulting EEG signals were characterised by extracting AGR coefficients. Classification was carried out using a Multilayer perceptron (MLP) trained with the classical backpropagation algorithm. Overall results show that AGR coefficients representation is able to reveal a significant EEG discrimination between imagination of right and left hand movement with a mean classification performance of 91%+/-5.8% achieved for female subjects and 87%+/-5.0% achieved for male subjects.

Adult↗

PubMedDate not supplied

Mining knowledge for HEp-2 cell image classification.

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns has recently been done manually by a human inspecting the slides with a microscope. In this paper, we present results on the analysis and classification of cells using image analysis and data mining techniques. Starting from a knowledge acquisition process with a human operator, we developed an image analysis and feature extraction algorithm. The collection of the dataset was done based on an expert's image reading and based on the automatic extracted features. A dataset containing 132 features for each entry was set up and given to a data mining algorithm to find out the relevant features among this large feature set and to construct the classification knowledge. The classifier was evaluated by cross validation. The results gave the expert new insights into the necessary features and the classification knowledge and show the feasibility of an automated inspection system.

Algorithms↗

PubMed1999

Classification of Arabidopsis thaliana gene sequences: clustering of coding sequences into two groups according to codon usage improves gene prediction.

While genomic sequences are accumulating, finding the location of the genes remains a major issue that can be solved only for about a half of them by homology searches. Prediction methods are thus required, but unfortunately are not fully satisfying. Most prediction methods implicitly assume a unique model for genes. This is an oversimplification as demonstrated by the possibility to group coding sequences into several classes in Escherichia coli and other genomes. As no classification existed for Arabidopsis thaliana, we classified genes according to the statistical features of their coding sequences. A clustering algorithm using a codon usage model was developed and applied to coding sequences from A. thaliana, E. coli, and a mixture of both. By using it, Arabidopsis sequences were clustered into two classes. The CU1 and CU2 classes differed essentially by the choice of pyrimidine bases at the codon silent sites: CU2 genes often use C whereas CU1 genes prefer T. This classification discriminated the Arabidopsis genes according to their expressiveness, highly expressed genes being clustered in CU2 and genes expected to have a lower expression, such as the regulatory genes, in CU1. The algorithm separated the sequences of the Escherichia-Arabidopsis mixed data set into five classes according to the species, except for one class. This mixed class contained 89 % Arabidopsis genes from CU1 and 11 % E. coli genes, mostly horizontally transferred. Interestingly, most genes encoding organelle-targeted proteins, except the photosynthetic and photoassimilatory ones, were clustered in CU1. By tailoring the GeneMark CDS prediction algorithm to the observed coding sequence classes, its quality of prediction was greatly improved. Similar improvement can be expected with other prediction systems.

Algorithms↗

PubMed2003

Near-infrared Raman spectroscopy of human coronary arteries: histopathological classification based on Mahalanobis distance.

OBJECTIVE: In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries. BACKGROUND DATA: Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities. MATERIALS AND METHODS: Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C. RESULTS: Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type. CONCLUSION: An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.

Algorithms↗

PubMed2001

The sequence determinants of cadherin molecules.

The sequence and structural analysis of cadherins allow us to find sequence determinants-a few positions in sequences whose residues are characteristic and specific for the structures of a given family. Comparison of the five extracellular domains of classic cadherins showed that they share the same sequence determinants despite only a nonsignificant sequence similarity between the N-terminal domain and other extracellular domains. This allowed us to predict secondary structures and propose three-dimensional structures for these domains that have not been structurally analyzed previously. A new method of assigning a sequence to its proper protein family is suggested: analysis of sequence determinants. The main advantage of this method is that it is not necessary to know all or almost all residues in a sequence as required for other traditional classification tools such as BLAST, FASTA, and HMM. Using the key positions only, that is, residues that serve as the sequence determinants, we found that all members of the classic cadherin family were unequivocally selected from among 80,000 examined proteins. In addition, we proposed a model for the secondary structure of the cytoplasmic domain of cadherins based on the principal relations between sequences and secondary structure multialignments. The patterns of the secondary structure of this domain can serve as the distinguishing characteristics of cadherins.

Algorithms↗