RESUMO
Abstract 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.
Resumen La osteoartritis (OA) es el tipo de artritis más común. OA es una enfermedad limitante que afecta a 1 de 10 adultos con 60 años o más. Las imágenes de rayos-x son una técnica de diagnóstico primario que permite conocer el estado de OA, las cuales el experto lee y cuantifica así la etapa de la enfermedad. El Diagnóstico Asistido por Computadora (CADx, por sus siglas en inglés) ha buscado automatizar el diagnóstico de OA para ayudar al radiólogo en la detección y control; sin embargo, el dolor provocado por la progresión de la enfermedad es dejado atrás. En este trabajo se propone un sistema de CADx que cuantifica la similitud bilateral de las rodillas de los pacientes, con el fin de correlacionar el grado de asimetría con el dolor. Inicialmente, las imágenes de las rodillas fueron alineadas usando el algoritmo B-spline para su registro, después, un conjunto de métricas estándar fue cuantificado; finalmente, con estas métricas se propone un modelo multivariado para predecir el dolor de rodilla desarrollado en 48 meses. La metodología fue validada con 131 pacientes obtenidos de la base de datos de la Osteoarthritis Initiative (OAI). Los resultados sugieren que las métricas pueden ser asociadas con los puntajes de KellgrenLawrence; además, los modelos predicen significativamente el dolor crónico de rodilla con: AUC 0.756, 0.704 y 0.7113, al inicio, un año y dos años después, respectivamente.
RESUMO
Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system.