Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain

Digital filter implementation over FPGA platform with LINUX OS

The embedded processors on FPGA's are a good tool to specific propose works. In this work we present how the FPGA is used to apply a Sobel filter to a set of images, also the step needed to set-up the entire system is described. An embedded processor, with a Linux distribution implemented is used to run a special compilation of C filter program, the filter is compared with the results obtained with a PC running the same filter, in the embedded system all the process runs in the FPGA and the exit file can be accessed by ftp or http server embedded into the Linux system

Digital filter implementation over FPGA platform with LINUX OS

The embedded processors on FPGA's are a good tool to specific propose works. In this work we present how the FPGA is used to apply a Sobel filter to a set of images, also the step needed to set-up the entire system is described. An embedded processor, with a Linux distribution implemented is used to run a special compilation of C filter program, the filter is compared with the results obtained with a PC running the same filter, in the embedded system all the process runs in the FPGA and the exit file can be accessed by ftp or http server embedded into the Linux system

Multi-seed texture synthesis to fast image patching

Actually we have a large number of devices which can take pictures, from a digital camera to a cell phone, one of the problems is that usually at moment to take the picture we don’t see some errors that can be present in the image until we observe it in a computer or printed image, this is a problem, because is almost impossible to recapture the moment in a new photo, to find a solution for this problem come the need implement a method to correct some of the defects that appear. In this paper we compare two texture synthesis methods and propose an algorithm to patch an original image, using a multi seed generated texture image

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

Front-End Design Guidelines for Infotainment Systems

This paper presents a set of front-end design guidelines intended to provide a starting point to designers of user interfaces for infotainment systems. The proposed approach suggests guidance on four dimensions inferred from state of the art such as crucial to achieve well designed automotive interfaces: a) Design; b) Interaction; c) Security; and d) Connectivity. Guidelines were thought by integrating conceptual-insights from Graphic Design; User Centered Design; Human-Machine Interfaces; Usability; and Human-Computer interaction. Additionally, were specified and structured to be used also as a comparing tool (Like Heuristic- Evaluation technique) to analyze front-end of existent infotainment systems. Said duality allowed to revise the pertinence of the proposal through a case study where 30 participants (25 regular users and 5 technicalexperts) compared suggested guidelines’ specification against interactions provided by the front–end of Mazda Connect© infotainment System. Obtained results suggested that setting of proposed guidelines was compatible with participants’ perceptions facilitating to identify pain-points on current design; thus, proposed guidance could scaffold base-insights for new front-end designs

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

Métricas de Registro de Imágenes y Predicción de Dolor de Rodilla por Osteoartritis Crónica: Datos de la Osteoarthritis Initiative

Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up.Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up