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Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission by Diego Miranda; Rodrigo Olivares; Roberto Munoz; Jean-Gabriel Minonzio is a Engineering article available to read on EtoBox.

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Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected using the X-Ray gold standard dual-energy X-ray absorptiometry (DXA), based on a calibrated 2-D image. Different alternatives, such as 3-D X-rays, magnetic resonance imaging (MRI) or ultrasound, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. Bidirectional axial transmission (BDAT) has been used to classify between patients with or without nontraumatic fractures using "classical" ultrasonic parameters, such as velocities, as well as cortical thickness and porosity, obtained from an inverse problems. Recently, complementary parameters acquired with structural and textural analysis of guided wave spectrum images (GWSIs) have been introduced. These parameters are not limited by solution ambiguities, as for inverse problem. The aim of the study is to improve the patient classification using a feature selection strategy for all available ultrasound features completed by clinical parameters. To this end, three classical feature ranking methods were considered: analysis

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Author

Diego Miranda; Rodrigo Olivares; Roberto Munoz; Jean-Gabriel Minonzio

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)