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This study introduces a deep neural network (DNN) for non-invasive bone fracture diagnosis using radio frequency waves. The DNN accurately classifies fracture types and estimates crack length, offering a portable solution without X-rays.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Traditional bone fracture diagnosis relies on X-rays, which involve ionizing radiation and require expert interpretation.
  • Existing automated methods often depend on labeled X-ray images, posing challenges in data collection and labeling.
  • There is a need for non-invasive, portable, and rapid diagnostic tools for bone fractures, especially in remote or emergency settings.

Purpose of the Study:

  • To investigate the feasibility of a deep neural network (DNN) for bone fracture diagnosis using non-invasive radio frequency (RF) wave propagation.
  • To develop a DNN model capable of classifying various complex fracture types and estimating crack length.
  • To create a portable diagnostic system that eliminates the need for X-rays and expert radiologists.

Main Methods:

  • Utilized S-parameters profiles, derived from RF wave propagation, for DNN training, bypassing the need for labeled X-ray images.
  • Developed a DNN architecture designed to simultaneously classify fracture types (normal, transverse, oblique, comminuted) and estimate crack length.
  • Created synthetic datasets by modeling human body tissues and varying diameters to simulate different anatomical regions.
  • Validated numerical results through experiments on sheep femur bones using liquid phantoms.

Main Results:

  • The developed DNN was successfully trained without overfitting, demonstrating robust performance on synthetic data.
  • Experimental validation confirmed the DNN's ability to accurately classify fracture types.
  • The system achieved accurate fracture classification without the use of ionizing radiation (X-rays).

Conclusions:

  • A DNN approach using RF wave propagation is a feasible and effective method for non-invasive bone fracture diagnosis.
  • The proposed system offers a portable, rapid, and radiation-free alternative for preliminary fracture assessment in diverse settings.
  • This technology has the potential to improve emergency medical care and accessibility to diagnostic tools in low-resource environments.