[Dyslipidemia in high cardiovascular risk patients. Initial results from the REMEXDIS-IMSS study].

Background: There is no national registry on dyslipidemia and low-density lipoprotein cholesterol (LDL-c) goals by risk groups for atherosclerotic cardiovascular disease (ACVD) focused on beneficiaries of the Mexican Institute for Social Security (IMSS).

Objective: To determine the frequency of dyslipidemia, LDL-c goals and patients in treatment from high and very high-risk groups of ACVD.

Material And Methods: Multicenter, cross-sectional, descriptive study.

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control.

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. : Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions.

[Macular edema in retinal fundus images by a computational algorithm].

Background: Diabetes is a metabolic disease highly prevalent in our country that ends in disabling complications such as diabetic retinopathy and macular edema. As a high-impact socioeconomic issue, it is important to look for an early diagnostic test that also allows us to introduce a telemedicine service for the population with little access to specialized health services.

Objective: To describe the performance in discrimination of macular edema of a feature detection algorithm on retinal fundus images from diabetic patients.

High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm.

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensity, texture and shape. The feature selection task is carried out on a high-dimensional feature bank, where the search space is denoted by O(2n) and n=473.

[Analysis of life quality on patients with thoracolumbar fractures].

Background: Patients with thoracolumbar fractures with TLICS 4 classification are at the limit of surgical fixation with regards to conservative treatment; however, results in our environment are not known, which is why this study has innovative characteristics.

Objective: To determine the quality of life in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation in a third level hospital.

Material And Methods: A cohort prospective study was carried out in patients with TLICS 4 classification thoracolumbar fractures using traditional fixation with regards to no fixation in beneficiaries from the Mexican Institute for Social Security.

[Atherogenic index and its association with acute coronary ischemic syndrome mortality].

Background: Several indexes have been developed to define the risk attributable to lipid metabolism with a single value. The total cholesterol/high-density lipoprotein (TC/HDL-C) and low-density lipoprotein/high-density lipoprotein (LDL-C/HDL-C) ratios are the most used. The higher the value of these ratios, the greater the probability of cardiovascular events.

[Comparative analysis of bleed volume with visual technique].

Background: Blood loss estimation in a surgery is made by anesthesiologists by means of visual technique, which is not reliable because it can change depending on the judgement of every person, or his/her work experience, which is why it is considered something subjective. Therefore, the results obtained could lead to make mistakes with the exact amount of bleeding, mismanaging unnecessary hemoderived transfusions or administering unnecessary drugs.

Objective: To compare the blood volume and its visual calculation between Anesthesiology residents and anesthesiologists.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images.

Background And Objective: Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the heart, increasing the risk of a heart attack. Convolutional Neural Networks (CNNs) have been successfully applied to identify pathological, regular, and featured tissues on rich and diverse medical image datasets.

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The proposed method consists of two different stages.

Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques.

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity are based on a qualitative interpretation by the physician; there is no quantitative interpretation of the footprint. The importance of providing a correct and accurate diagnosis lies in the need to ensure that an appropriate treatment is provided for the improvement of the patient without risking his or her health.

Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution.

Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed.

Fast Parabola Detection Using Estimation of Distribution Algorithms.

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function.

Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm.

This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area ( ) under the receiver operating characteristic curve is used as fitness function.

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data.

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels.

Unsupervised cardiac image segmentation via multiswarm active contours with a shape prior.

This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour.

Multiple active contours guided by differential evolution for medical image segmentation.

This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced.

Multiple active contours driven by particle swarm optimization for cardiac medical image segmentation.

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented.