Improved parkinsonism diagnosis using a partial least squares based approach.

PURPOSE: An accurate and early diagnosis of Parkinsonian syndrome (PS) is nowadays a challenge. This syndrome includes several pathologies with similar symptoms (Parkinson's disease, multisystem atrophy, progressive supranuclear palsy, corticobasal degeneration and others) which make the diagnosis more difficult. (123)I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy. METHODS: In this paper, we show a novel method to automatically classify (123)I-ioflupane images into two groups: controls or PS. The proposed methodology analyzes separately each hemisphere of the brain by means of a novel approach based on partial least squares (PLS) and support vector machine. RESULTS: A database with 189 (123)I-ioflupane images (94 controls and 95 pathological images) was used for evaluation purposes. The application of the proposed method based on PLS yields high accuracy rates up to 94.7% with sensitivity = 93.7% and specificity = 95.7%, outperforming previous approaches based on singular value decomposition, which are used as a reference. CONCLUSIONS: The use of advanced techniques based on classical signal analysis and their application to each hemisphere of the brain separately improves the (assisted) diagnosis of PS.

Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging.

PURPOSE: In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images. METHODS: Ioflupane[(123)I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brain images in combination with voxel selection masks. RESULTS: The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brain images are used. The generalization performance is estimated to be 89.02 (90.41-87.62)% sensitivity and 93.21 (92.24-94.18)% specificity. The area under the curve can take values of 0.9681 (0.9641-0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves. CONCLUSIONS: The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust.

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.

Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images.

PURPOSE: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. METHODS: The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. RESULTS: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel. CONCLUSIONS: Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.

Antiphospholipid syndrome: a comprehensive review of a complex and multisystemic disease.

The antiphospholipid syndrome (APS) is an acquired thombophilia, which is characterized by one or more thrombotic episodes and obstetric complications in the presence of antiphospholipid (aPL) antibodies (Abs). APL Abs are detected by laboratory tests such as lupus anticoagulant (LAC), anticardiolipin (aCL) and anti-Beta2-glycoprotein I (Beta2GPI) Abs. This article reviews the most current aspects of APS with emphasis on the pathophysiology of the disease, clinical manifestations, laboratory tests, and current modalities of treatment.

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above 80% on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism (N = 120, n = 30/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the "extreme male brain" theory of autism, in sexual dimorphic areas.

Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders.

The analysis of neuroimaging data is frequently used to assist the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) or Parkinson's disease (PD) and has become a routine procedure in the clinical practice. During the past decade, the pattern recognition community has proposed a number of machine learning-based systems that automatically analyse neuroimaging data in order to improve the diagnosis. However, the high dimensionality of the data is still a challenge and there is room for improvement. The development of novel classification frameworks as TensorFlow, recently released as open source by Google Inc., represents an opportunity to continue evolving these systems. In this work, we demonstrate several computer-aided diagnosis (CAD) systems based on Deep Neural Networks that improve the diagnosis for AD and PD and outperform those based on classical classifiers. In order to address the small sample size problem we evaluate two dimensionality reduction algorithms based on Principal Component Analysis and Non-Negative Matrix Factorization (NNMF), respectively. The performance of developed CAD systems is assessed using 4 datasets with neuroimaging data of different modalities.

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. METHOD: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. RESULTS: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects. COMPARISON WITH EXISTING METHOD(S): The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning. CONCLUSIONS: A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease.

In this work, we present a fully automatic computer-aided diagnosis method for the early diagnosis of the Alzheimer's disease. We study the distance between classes (labelled as normal controls and possible Alzheimer's disease) calculated in 116 regions of the brain using the Welchs's t-test. We select the regions with highest Welchs's t-test value as features to perform classification. Furthermore, we also study the less discriminative region according to the t-test (regions with lowest t-test absolute values) in order to use them as reference. We show that the mean and standard deviation of the intensity values in these two regions, the less and most discriminative according to the Welch's ttest, can be combined as a vector. The modulus and phase of this vector reveal statistical differences between groups which can be used to improve the classification task. We show how they can be used as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70 SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.

[Geriatric emergencies versus adult emergencies: retrospective analysis of medical emergencies at a general hospital]. / La urgencia geriátrica frente a la urgencia en la edad adulta: análisis retrospectivo de las urgencias médicas de un hospital general.

The aim of the study was to evaluate the process of the attention to emergencies in patients older than 65 years and to compare it with the same process in adult patients. To this end, 965 clinical records of medical emergencies from the Hospital Central de la Cruz Roja in Madrid were retrospectively evaluated, and data were obtained regarding age, the cause for consultation, the investigations performed and their yield, the administration of drug therapy, the major diagnosis at the time of discharge from the service and the clinical course. It was found that all evaluated diagnostic investigations were carried out with equal or higher frequency in patients older than 65 years and that their mean clinical effectiveness was also higher. In addition, it was found that the patients older than 65 years were more commonly admitted to the hospital through the emergency service than the rest of the population. It was concluded, therefore, that the process of attention to emergencies has differential characteristics in the elderly population, and that if the number and proportion of old people increase as it will presumably happen during the two next decades, the cost of attention to emergencies and the number of emergency hospital admissions will also increase.

[Variability of polyphenol content in different types of wine and its potential application in the understanding of its biologic effects]. / Variabilidad del contenido de polifenoles de distintos tipos de vino y su potencial aplicación al conocimiento de sus efectos biológicos.

BACKGROUND: Wine polyphenols have antioxidant properties. Different polyphenols have various biological activities on atherogenesis and carcinogenesis. MATERIAL AND METHODS: The composition on 5 polyphenols of 16 wines of Castilla (Spain) is determined by HPLC. RESULTS: Polyphenols concentrations varied largely among the different wines. Most red wines had higher amounts of polyphenols than white wines. CONCLUSIONS: The diverse composition on polyphenols of each wine allows to suggest different biological effects.

[Magnesium: therapeutic usefulness in emergency situations]. / El magnesio: utilidad terapéutica en situaciones de urgencia.

Magnesium has been a forgotten cation from the therapeutical point of view, given that, although its properties began to be known more than a century ago, its use has been always empirical or isolated. With respect to its use in emergency situations, in addition to correcting its deficit and using it for the treatment of hypercalcemia and hypopotassemia, it is currently recommended as the therapy of choice for the treatment and prevention of several arrhythmias. It can also be used to prevent its deficit, when such deficit is pathological or associated to the ingestion or certain drugs. Given the multiple properties of magnesium, controlled studies are required in order to define its potential therapeutical applications.

[Pleural empyema due to group-A beta-hemolytic Streptococcus]. / Empiema pleural por streptococcus beta-hemolítico grupo A.

We present the case of a parenteral drug addict with pleural overflow (empyema), in which hemolytic Streptococcus group A was isolated. The absence of an early diagnosis and an effective empirical treatment caused the development of septic shock and the patient died in spite of the therapeutical measures implanted in the emergency unit.

[Autoimmune hemolytic anemia: a rare complication of ulcerative colitis]. / Anemia hemolítica autoinmune: una rara complicación de la colitis ulcerosa.

Anaemia is a common problem in patients with ulcerative colitis, and its etiology is usually multifactorial. It can be produced by chronic blood loss, nutritional deficiencies, drugs such as salazopyrine, or it can be related to those chronic disease. However, ulcerative colitis is known to be associated with several immune disorders, as autoimmune haemolytic anaemia. Nevertheless, this rare complication can be found in 0.2 to 0.7% of patients affected by ulcerative colitis, and although 1.82% of patients with ulcerative colitis have a positive direct Coombs test without evidence of hemolysis. We report one new case of ulcerative colitis associated with autoimmune anaemia haemolytic, and a review the literature.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems.

Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees.

This paper presents a computer-aided diagnosis technique for improving the accuracy of early diagnosis of Alzheimer-type dementia. The proposed methodology is based on the selection of voxels which present Welch's t-test between both classes, normal and Alzheimer images, greater than a given threshold. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task.

Classification of functional brain images using a GMM-based multi-variate approach.

This paper presents a novel method for automatic selection of regions of interest (ROIs) of functional brain images based on Gaussian mixture models (GMM), which relieves the so-called small size sample problem in the classification of functional brain images for the diagnosis of Alzheimer's disease (AD). In a first step, brain images are preprocessed in order to find an average image including differences between controls and AD patients. Then, ROIs are extracted using a GMM which is adjusted by using the expectation maximization (EM) algorithm. This reduced set of features provides the activation map of each patient and allows us to train statistical classifiers based on support vector machines (SVMs). The leave-one-out cross-validation technique is used to validate the results obtained by the supervised learning-based computer aided diagnosis (CAD) system over databases of SPECT and PET images yielding an accuracy rate up to 96.67%.

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction.

This letter presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of Alzheimer's disease (AD) based on non-negative matrix factorization (NMF) analysis applied to single photon emission computed tomography (SPECT) images. A baseline normalized SPECT database containing normalized data for both AD patients and healthy reference patients is selected for this study. The SPECT database is analyzed by applying the Fisher discriminant ratio (FDR) for feature selection and NMF for feature extraction of relevant components of each subject. The main goal of these preprocessing steps is to reduce the large dimensionality of the input data and to relieve the so called "curse of dimensionality" problem. The resulting NMF-transformed set of data, which contains a reduced number of features, is classified by means of a support vector machines based classification technique (SVM). The proposed NMF + SVM method yields up to 94% classification accuracy, with high sensitivity and specificity values (upper than 90%), becoming an accurate method for SPECT image classification. For the sake of completeness, comparison between another recently developed principal component analysis (PCA) plus SVM method and the proposed method is also provided, yielding results for the NMF + SVM approach that outperform the behavior of the reference PCA + SVM or conventional voxel-as-feature (VAF) plus SVM methods.