Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Saving our science from ourselves: the plight of biological classification

Saving our science from ourselves: the plight of biological classification. Biological classification ( nomenclature, taxonomy, and systematics) is being sold short. The desire for new technologies, faster and cheaper taxonomic descriptions, identifications, and revisions is symptomatic of a lack of appreciation and understanding of classification. The problem of gadget-driven science, a lack of best practice and the inability to accept classification as a descriptive and empirical science are discussed. The worst cases scenario is a future in which classifications are purely artificial and uninformative.

A data-dependent skeleton estimate and a scale-sensitive dimension for classification

The classical binary classification problem is investigatedwhen it is known in advance that the posterior probability function(or regression function) belongs to some class of functions. We introduceand analyze a method which effectively exploits this knowledge. The methodis based on minimizing the empirical risk over a carefully selected``skeleton'' of the class of regression functions. The skeleton is acovering of the class based on a data--dependent metric, especiallyfitted for classification. A new scale--sensitive dimension isintroduced which is more useful for the studied classification problemthan other, previously defined, dimension measures. This fact isdemonstrated by performance bounds for the skeleton estimate in termsof the new dimension.

Couple and family treatments: study quality and level of evidence.

This paper examines the application of the guidelines for evidence-based treatments in family therapy developed by Sexton and collaborators to a set of treatment models. These guidelines classify the models using criteria that take into account the distinctive features of couple and family treatments. A two-step approach was taken: (1) The quality of each of the studies supporting the treatment models was assessed according to a list of ad hoc core criteria; (2) the level of evidence of each treatment model was determined using the guidelines. To reflect the stages of empirical validation present in the literature, nine models were selected: three models each with high, moderate, and low levels of empirical validation, determined by the number of randomized clinical trials (RCTs). The quality ratings highlighted the strengths and limitations of each of the studies that provided evidence backing the treatment models. The classification by level of evidence indicated that four of the models were level III, "evidence-based" treatments; one was a level II, "evidence-informed treatment with promising preliminary evidence-based results"; and four were level I, "evidence-informed" treatments. Using the guidelines helped identify treatments that are solid in terms of not only the number of RCTs but also the quality of the evidence supporting the efficacy of a given treatment. From a research perspective, this analysis highlighted areas to be addressed before some models can move up to a higher level of evidence. From a clinical perspective, the guidelines can help identify the models whose studies have produced clinically relevant results.

Quality assessment of cardiovascular magnetic resonance in the setting of the European CMR registry: description and validation of standardized criteria.

BACKGROUND: Cardiovascular magnetic resonance (CMR) has become an important diagnostic imaging modality in cardiovascular medicine. However, insufficient image quality may compromise its diagnostic accuracy. We aimed to describe and validate standardized criteria to evaluate a) cine steady-state free precession (SSFP), b) late gadolinium enhancement (LGE), and c) stress first-pass perfusion images. These criteria will serve for quality assessment in the setting of the Euro-CMR registry. METHODS: Thirty-five qualitative criteria were defined (scores 0-3) with lower scores indicating better image quality. In addition, quantitative parameters were measured yielding 2 additional quality criteria, i.e. signal-to-noise ratio (SNR) of non-infarcted myocardium (as a measure of correct signal nulling of healthy myocardium) for LGE and % signal increase during contrast medium first-pass for perfusion images. These qualitative and quantitative criteria were assessed in a total of 90 patients (60 patients scanned at our own institution at 1.5T (n=30) and 3T (n=30) and in 30 patients randomly chosen from the Euro-CMR registry examined at 1.5T). Analyses were performed by 2 SCMR level-3 experts, 1 trained study nurse, and 1 trained medical student. RESULTS: The global quality score was 6.7±4.6 (n=90, mean of 4 observers, maximum possible score 64), range 6.4-6.9 (p=0.76 between observers). It ranged from 4.0-4.3 for 1.5T (p=0.96 between observers), from 5.9-6.9 for 3T (p=0.33 between observers), and from 8.6-10.3 for the Euro-CMR cases (p=0.40 between observers). The inter- (n=4) and intra-observer (n=2) agreement for the global quality score, i.e. the percentage of assignments to the same quality tertile ranged from 80% to 88% and from 90% to 98%, respectively. The agreement for the quantitative assessment for LGE images (scores 0-2 for SNR <2, 2-5, >5, respectively) ranged from 78-84% for the entire population, and 70-93% at 1.5T, 64-88% at 3T, and 72-90% for the Euro-CMR cases. The agreement for perfusion images (scores 0-2 for %SI increase >200%, 100%-200%,<100%, respectively) ranged from 81-91% for the entire population, and 76-100% at 1.5T, 67-96% at 3T, and 62-90% for the Euro-CMR registry cases. The intra-class correlation coefficient for the global quality score was 0.83. CONCLUSIONS: The described criteria for the assessment of CMR image quality are robust with a good inter- and intra-observer agreement. Further research is needed to define the impact of image quality on the diagnostic and prognostic yield of CMR studies.

Natural classification of knots

The principal objective of the knot theory is to provide a simple way of classifying and ordering all the knot types. Here, we propose a natural classification of knots based on their intrinsic position in the knot space that is defined by the set of knots to which a given knot can be converted by individual intersegmental passages. In addition, we characterize various knots using a set of simple quantum numbers that can be determined upon inspection of minimal crossing diagram of a knot. These numbers include: crossing number; average three-dimensional writhe; number of topological domains; and the average relaxation value

Atypical EPO profiles in anti-doping analyses

Résumé : Erythropoietin (EPO) is a glycoprotein hormone endogenously produced by the kidney, whose main physiological role is the stimulation of erythropoiesis. Since the beginning of the nineties, recombinant human EPO (rhEPO), a potent anti-anaemia treatment drug, has been manufactured by pharmaceutical industries. However, the erythropoiesis stimulating power of rhEPO was rapidly misused by unscrupulous athletes in order to improve their performances in endurance sports. Endogenous EPO has the same amino-acid backbone as most of recombinant forms; the molecules however differ through their respective glycosylation patterns. This difference constitutes the basis of the usual EPO screening test (IEF) developed in 2000 and still currently used in all anti-doping laboratories of the world. Nowadays, 3 EPO generations have been commercialized. The fight against EPO abuse is a continuous challenge for anti-doping laboratories. The diversity of recombinant EPO forms and the continuous development of new ones considerably confuse the identification of EPO doping. Several facets of this fight were investigated in this work. One of the limiting aspects of doping agents screening is the availability of positive samples. Therefore, 2nd and 3rd generation EPOS, namely NESP and C.E.R.A., were injected to healthy subjects in the frame of pilot clinical studies. These latter allowed to review the current EPO identification criteria defined by the World Anti-Doping Agency (WADA) in the case of NESP and to validate and implement a new assay targeting C.E.R.A. in human serum. Both studies resulted in the determination of the respective detection windows of NESP and C.E.R.A. in biological fluids. Following that, Dynepo, a 1st generation EPO presenting similarities with the endogenous form, was also in the centre of a similar clinical study. Our work aimed to overcome the actual identification criteria, which are not adapted to Dynpeo, and to propose an alternative pattern classification method based on the discriminant analysis of IEF EPO profiles. This method might be validated for other EPO forms in the future. The detection window of this molecule was also determined. Under particular conditions, confounding effects can complicate the identification of EPO in biological matrices. For example, athletes having performed a strenuous physical effort can excrete modified isoforms of endogenous EPO, making it very similar to some recombinant forms. Such phenomena, called effort urines, were reproduced under controlled conditions and, after characterization of effort EPO, an urinary biochemical marker was proposed to unequivocally identify effort urines. It also happens that EPO analyses fail to detect endogenous levels of EPO. Such profiles were thoroughly investigated and potential causes identified. Natural reasons relying on urine properties and test specificity were underlined, but the possible addition of adulterant agents in urine samples was also considered. Therefore, a simple biochemical assay targeting the suspected substances was set up. Our work was based on the characterization of atypical EPO profiles from different origins. Therefore, 3 EPO molecules representing the 3 generations of the drug and 2 confounding effects confusing the results interpretation were studied. These studies resulted in tangible applications for the laboratory, the best example of which being the C.E.R.A. assay, but also in scientific findings allowing to improve our comprehension of EPO doping in sport.

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 2 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Minimal data requirements for a continuous monitoring of the quality of care using the DRG classification.

The DRG classification provides a useful tool for the evaluation of hospital care. Indicators such as readmissions and mortality rates adjusted for the hospital Casemix could be adopted in Switzerland at the price of minor additions to the hospital discharge record. The additional information required to build patients histories and to identify the deaths occurring after hospital discharge is detailed.