Random Forest-Based Prediction of Bone Volume Fraction and Fracture Position from S-Parameters

2026-07-26

AI Summary

This study proposes a non-invasive method for assessing skeletal status using microwave signals to accurately predict bone volume fraction and fracture location. A nine-antenna microwave scanning system is employed to acquire multi-channel S-parameters, which are then integrated into a random forest model for prediction. This work represents the first application of this algorithm combined with multi-channel microwave features for bone health evaluation. Validated on both synthetic and experimental data using a bone-mimicking phantom platform, the system demonstrates high accuracy and feasibility in non-invasively estimating bone parameters, offering a novel approach for the early diagnosis of osteoporosis and fractures.

Abstract

In this paper, we propose a method for predicting bone volume fraction (BVF) and fracture position by constructing a random forest model based on multichannel S-parameters. A nine-antenna microwave scanning system is designed and fabricated to acquire the multichannel S-parameter data. Bone-mimicking phantoms are developed, and corresponding experiments are conducted to validate the effectiveness of the proposed approach. Both synthetic and experimental results demonstrate the validity of the method.

Problem

• bone volume fraction
• fracture position
• S-parameters
• microwave scanning
• bone-mimicking phantoms

Innovation

• Random Forest
• S-parameters
• Bone Volume Fraction
• Fracture Localization
• Microwave Imaging