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

arXiv: https://arxiv.org/abs/2607.23563 · HTML: https://mlsanity.com/paper/2607.23563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)

## Abstract (arXiv)

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.