Comparison of Resampling Methods and Radiomic Machine Learning Classifiers for Predicting Bone Quality Using Dual-Energy X-Ray Absorptiometry

Comparison of Resampling Methods and Radiomic Machine Learning Classifiers for Predicting Bone Quality Using Dual-Energy X-Ray Absorptiometry

<b>Background/Objectives</b>: This study presents a novel approach, based on a combination of radiomic feature extraction, data resampling techniques, and machine learning algorithms, for the detection of degraded bone structures in Dual X-ray Absorptiometry (DXA) images. This comprehens...

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Bibliographic Details
Main Authors: Mailen Gonzalez, José Manuel Fuertes García, María Belén Zanchetta, Rubén Abdala, José María Massa
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Diagnostics
Subjects:
dual energy X-ray absorptiometry
trabecular bone score
radiomics
data resampling
machine learning
Online Access:https://www.mdpi.com/2075-4418/15/2/175
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