Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.

[Renal pathophysiology and pharmacological mechanisms of nephroprotection]. / Fisiopatología renal y mecanismos farmacológicos de nefroprotección.

Several risk factors may affect the progression of chronic kidney disease (CKD). Arterial hypertension, proteinuria, obesity, intraglomerular hypertension, smoking and metabolic control in diabetes mellitus are the main modifiable risk factors for progression. The progression of CKD involves many cellular processes that originate in specific compartments of the kidney, the vascular compartment with nephroangiosclerosis and the tubulointerstitial compartment with fibrosis and tubulointerstitial atrophy, and there may be overlap between both mechanisms. Given the involvement of so many risk factors and so many pathogenic pathways in the progression of CKD, the best hope for delaying or preventing the progression of CKD lies in a combined and multidisciplinary therapeutic approach, based on the existing evidence and acting on all these processes and pathways from the mechanistic point of view, and on a global process that is cardiovascular and renal risk to improve the prognosis of patients.

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.

Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team.

The use of artificial intelligence (AI) algorithms in the field of radiology is becoming more common. Several studies have demonstrated the potential utility of machine learning (ML) and deep learning (DL) techniques as aids for radiologists to solve specific radiological challenges. The decision-making process, the establishment of specific clinical or radiological targets, the profile of the different professionals involved in the development of AI solutions, and the relation with partnerships and stakeholders are only some of the main issues that have to be faced and solved prior to starting the development of radiological AI solutions. Among all the players in this multidisciplinary team, the communication between radiologists and data scientists is essential for a successful collaborative work. There are specific skills that are inherent to radiological and medical training that are critical for identifying anatomical or clinical targets as well as for segmenting or labelling lesions. These skills would then have to be transferred, explained, and taught to the data science experts to facilitate their comprehension and integration into ML or DL algorithms. On the other hand, there is a wide range of complex software packages, deep neural-network architectures, and data transfer processes for which radiologists need the expertise of software engineers and data scientists in order to select the optimal manner to analyse and post-process this amount of data. This paper offers a summary of the top five challenges faced by radiologists and data scientists including tips and tricks to build a successful AI team.

[COVID-19 and cardiovascular and kidney disease: Where are we? Where are we going?] / COVID-19 y enfermedad cardiovascular y renal: ¿Dónde estamos? ¿Hacia dónde vamos?

The SARS-CoV-2 pandemic is a global health emergency and we need to know more about it. Patients with cardiovascular risk and previous kidney risk have been identified as especially vulnerable for greater morbidity and mortality when they suffer from COVID-19. A considerable proportion of patients can develop a vascular lesion in the context of the disease that entails a greater lethality. Cardiovascular and renal complications represent a problem and, probably in the near future, may pose a threat to patients who have survived COVID-19. As physicians, we cannot forget that during an epidemic like this, other chronic diseases are present, and patients continue to require care. We are obliged to monitor even more intensely their treatments and control degree. Furthermore, we must not forget that urgent situations continue to arise in this pandemic situation and require prompt attention. In this current situation, it is very likely that many patients, out of fear, have not sought medical attention. The situation during the epidemic and the uncertainty of the post-COVID-19 period, requires intensification in the control and monitoring of cardiovascular and kidney disease in our patients. Primary care constitutes a key level of care for the care of the population with cardiovascular disease. Likewise, and in the face of this new health scenario, we need to promote the prevention and control measures that emanate from the studies currently underway. Now, more than ever, we need research, crucial to improve the cardiovascular and renal prognosis of our patients.

[Positioning for the management of arterial hypertension in Primary Care from the critical analysis of the American (2017) and European (2018) guidelines. Spanish Society of Primary Care Physicians (SEMERGEN)]. / Posicionamiento para el manejo de la hipertensión arterial en atención primaria a partir del análisis crítico de las guías americana (2017) y europea (2018). Sociedad Española de Médicos de Atención Primaria (SEMERGEN).

The Scientific Societies of Primary Care, being the area in which there is a considerable prevalence of Arterial Hypertension (AHT), need to periodically evaluate the international guidelines for its management. This is particularly relevant when disparate guidelines make it difficult to make decisions in daily clinical practice. The present document has as its aim to analyse the changes and new developments proposed in the guidelines of the American College of Cardiology and the American Heart Association (ACC/AHA 2017), as well as in the guidelines of the European Society of Cardiology and European Society of Hypertension (ESC/ESH 2018). An analysis will be made of any differences, limitations, and their applicability to Primary Care in Spain. Finally, the most relevant available and appropriate information is extracted and integrated in order to homogenise the care of the hypertensive patient, from a critical, but also a reasoned, perspective. The discrepancies between the recommendations in such essential aspects as the management of the disease, require the compiling and critical analysis of the information that enables us as scientific society, interested in providing all PC physicians with the most relevant, and at the same time, sensible, recommendations of all the guidelines.

[Tools to improve efficiency in the clinical management of hypertensive patients]. / Herramienta para mejorar la eficiencia en el manejo clínico de pacientes hipertensos.

OBJECTIVE: To create a tool to evaluate the efficiency of the clinical management of hypertensive patients in Primary Care. MATERIAL AND METHODS: A web-based questionnaire was designed for Primary Care centres to self-evaluate the management of hypertension in five specific areas: information systems, diagnostic and analytical tests, organisational aspects, use of resources, and continuous training programmes for patients and healthcare professionals. A committee of experts previously defined these questions and their ideal responses or "control", based on the scientific literature or, if there were no published references, by consensus of the committee. A descriptive analysis was performed on the data, and an adherence score was created that ranged from 0 (no adherence) to 1 (total adherence). RESULTS: A total of 35 Primary Care centres entered their data into the website for the clinical management of hypertensive patients. The highest adherence to the ideal algorithm was observed in the area "Diagnostic and analytical tests" (0.69±0.10), and the lowest in "Continuous training programmes for patients and professionals" (0.42±0.21). CONCLUSIONS: The efficiency of clinical management in hypertensive patients can be analysed using the website tool created for this purpose. Its use allows an internal audit to detect the areas that need improvement, and also serves to make comparative evaluations in the different areas of management over time.

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.

Sodium zirconium cyclosilicate: a new potassium binder for the treatment of hyperkalemia.

Hyperkalemia is one of the most common electrolyte disturbances, especially among some groups of patients, such as in those with chronic kidney disease, diabetes or heart failure. Hyperkalemia has been associated with increased risks of mortality, arrhythmias, hospitalization and costs, as well as the need to down titrate/discontinue renin-angiotensin-aldosterone system inhibitors (RAASIs), despite their well-known cardiovascular and nephroprotective benefits. Current potassium binders have limitations (slow onset of action, limited selectivity for potassium binding, risk of drug interactions or gastrointestinal intolerance). Sodium zirconium cyclosilicate (SZC) is a new potassium binder recently approved for the treatment of chronic hyperkalemia. It is a nonabsorbable, inorganic crystal which selectively binds potassium and ammonium in exchange of Na+ and H+ in the whole gastrointestinal tract, achieving a rapid correction of serum potassium levels (within 2 days) and maintaining normokalemia in the long term (up to 1 year), with a good safety profile (common adverse reactions include gastrointestinal events and a dose-dependent risk of edema), excellent tolerability and a low potential for drug interactions. Its potassium-lowering efficacy is maintained irrespective of the use of RAASIs. In summary, SZC is a new potassium binder recently approved for the treatment of hyperkalemia. Its differences with respect to currently available potassium binders make SZC an attractive therapeutic option.

[Anemia of chronic kidney disease: Protocol of study, management and referral to Nephrology]. / Anemia en la enfermedad renal crónica: protocolo de estudio, manejo y derivación a Nefrología.

The objective of this protocol is to know which test are needed to study an anaemia in a patient with chronic kidney disease, the differential diagnosis of renal anaemia, to know and correct other deficiency anaemias, and the criteria for referral to Nephrology or other specialties of the anaemic patient with chronic kidney disease.

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.

Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.

[Medical expert consensus in AH on the clinical use of triple fixed-dose antihypertensive therapy in Spain]. / Consenso de médicos expertos en HTA sobre el uso clínico de la triple terapia antihipertensiva a dosis fija en España.

INTRODUCTION: The opinion of experts (different specialties) on the triple fixed-dose antihypertensive therapy in clinical practice may differ. MATERIALS AND METHODS: Online questionnaire with controversial aspects of the triple therapy answered by panel of experts in hypertension (HT) using two-round modified Delphi method. RESULTS: The questionnaire was completed by 158 experts: Internal Medicine (49), Nephrology (26), Cardiology (83). Consensus was reached (agreement) on 27/45 items (60%); 7 items showed differences statistically significant. Consensus was reached regarding: Predictive factors in the need for combination therapy and its efficacy vs. increasing the dose of a pretreatment, and advantage of triple therapy (prescription/adherence/cost/pressure control) vs. free combination. CONCLUSIONS: This consensus provides an overview of the clinical use of triple therapy in moderate-severe and resistant/difficult to control HT.

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.

A Spherical Brain Mapping of MR Images for the Detection of Alzheimer's Disease.

Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer's Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.