Examinando por Autor "Miranda, Diego"

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  • Miniatura
    Ítem
    A Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification
    (IEEE, 2021) Miranda, Diego; Olivares, Rodrigo; Muñoz, Roberto; Jean-Gabriel Minonzio
    Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. Optimization was done using the Particle Swarm Optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.
  • Miniatura
    Ítem
    Selection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Leaming Strategy
    (IEEE, 2021) Miranda, Diego; Olivares, Rodrigo; Munoz, Roberto; Minonzio, Jean-Gabriel
    Osteoporosis is a widespread public health problem worldwide, characterized by low bone mass, which compromises strength and increases the risk of fracture. Currently, the gold standard for assessing fracture risk is measurement of areal bone mineral density with dual-energy X-ray absorptiometry (DXA). Several ultrasound techniques, such as Bi-Directional Axial Transmission (BDAT) have been presented as alternatives. For the first studies, classification between fractured and non fractured patients was based on classical ultrasonic parameters, such as velocities or cortical thickness and porosity, obtained from an inverse problem. Recently, novel parameters obtained from structural analysis guided wave spectrum images (GWSI) have been introduced. The aim of this study is to merge both points of view and explore which parameters are the most important to obtain a robust classification using a machine learning approach. This study uses the same set of patients used in previous studies with 195 patients associated with 8 ultrasonic parameters and 3 clinical factors (age BMI and cortisone intake). In addition, each patient corresponds to 10 GWSI, from which 32 parameters of structural analysis are extracted per image, leading to a total of 43 features per image. The dataset was divided into 70% of patients (n = 136) as training and 30% as testing (n = 59). The distribution of patients was adjusted for age and target class. The accuracy was calculated for an increased number of features, which ranking was obtained using Recursive Feature Elimination (RFE). The highest accuracy of 71% is obtained with the optimized parameters and a combination between 22 and 25 features. These result, comparable to femoral DXA (AUC = 0.71, adjusted linear regression), opens perspective towards robust detection of patients at risk of fracture with ultrasound.