Selection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Leaming Strategy

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dc.contributor.authorMiranda, Diego
dc.contributor.authorOlivares, Rodrigo
dc.contributor.authorMunoz, Roberto
dc.contributor.authorMinonzio, Jean-Gabriel
dc.date.accessioned2022-11-30T02:46:36Z
dc.date.available2022-11-30T02:46:36Z
dc.date.issued2021
dc.description.abstractOsteoporosis 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.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameMiranda_Sel2021.pdf
dc.identifier.citationD. Miranda, R. Olivares, R. Munoz and J. -G. Minonzio, "Selection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Learning Strategy," 2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS), 2021, pp. 1-3, doi: 10.1109/LAUS53676.2021.9639108.en_ES
dc.identifier.doi10.1109/LAUS53676.2021.9639108.
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7434
dc.languageen
dc.publisherIEEE
dc.source2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)
dc.subjectDEEP LEARNINGen_ES
dc.subjectTRAININGen_ES
dc.subjectULTRASONIC IMAGINGen_ES
dc.subjectINVERSE PROBLEMSen_ES
dc.subjectDATABASESen_ES
dc.subjectULTRASONIC VARIABLES MEASUREMENTen_ES
dc.subjectBIDIRECTIONAL CONTROLen_ES
dc.titleSelection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Leaming Strategy
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
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