Skull fracture detection for point-of-care diagnostics using microwave technique

The possibility to detect severity of skull fractures outside the hospital already in the accident location would be a promising eHealth application. Such a point-of-care type of device would smoothen the patients’ path to the treatment after the accident. Additionally, frequent and safe monitoring of the healing process of skull fractures in smaller healthcare centers, as a new telemedicine solution, would enable early detection of potential problems. Microwave technique has shown significant potential for portable monitoring devices since it enables safe, low-cost, and high accuracy detectors. This paper has three research objectives: (1) to investigate the potential of microwave techniques for detecting common skull fracture types using antennas embedded in a small, portable monitoring device, (2) to evaluate optimal frequency ranges for skull fracture detection, and (3) to examine the impact of scalp thickness on fracture detectability. The research is carried out using electromagnetic simulation software with human head models resembling different scalp thicknesses and flexible antennas operating at 2.5–10 GHz. In the evaluations, scattering parameters are analyzed with the head models both with and without the fractures. The evaluation results show that different types of skull fractures can be detected with microwave technique. Fractures cause differences in scattering parameters ranging from 2 dB to 12 dB, depending on the type of fracture. The most optimal frequency ranges for detection are 3–5 GHz and 9 GHz. The thickness of the scalp impacts the detectability of skull fractures, yet fractures remain detectable even with the model with thicker scalp. These findings support the potential of microwave technique for developing portable, point-of-care devices for timely and precise skull fracture pre-diagnosis and frequent monitoring of the healing process. This approach particularly benefits young children, for whom conventional screening methods may pose more significant challenges.

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Publication Details

Journal
Finnish Journal of eHealth and eWelfare
Published
2025-04-08
DOI
https://doi.org/10.23996/fjhw.155481
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36
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article

Skull fracture detection for point-of-care diagnostics using microwave technique

Mariella Särestöniemi, Teemu Myllylä, Daljeet Singh, Jarmo Reponen et al.
1 citations
Finnish Journal of eHealth and eWelfare
COVID-19 diagnosis using AI
1.36
article

Skull fracture detection for point-of-care diagnostics using microwave technique

Mariella Särestöniemi, Teemu Myllylä, Daljeet Singh, Jarmo Reponen, Mikael von und zu Fraunberg
article en
1 citations

Abstract

The possibility to detect severity of skull fractures outside the hospital already in the accident location would be a promising eHealth application. Such a point-of-care type of device would smoothen the patients’ path to the treatment after the accident. Additionally, frequent and safe monitoring of the healing process of skull fractures in smaller healthcare centers, as a new telemedicine solution, would enable early detection of potential problems. Microwave technique has shown significant potential for portable monitoring devices since it enables safe, low-cost, and high accuracy detectors. This paper has three research objectives: (1) to investigate the potential of microwave techniques for detecting common skull fracture types using antennas embedded in a small, portable monitoring device, (2) to evaluate optimal frequency ranges for skull fracture detection, and (3) to examine the impact of scalp thickness on fracture detectability. The research is carried out using electromagnetic simulation software with human head models resembling different scalp thicknesses and flexible antennas operating at 2.5–10 GHz. In the evaluations, scattering parameters are analyzed with the head models both with and without the fractures. The evaluation results show that different types of skull fractures can be detected with microwave technique. Fractures cause differences in scattering parameters ranging from 2 dB to 12 dB, depending on the type of fracture. The most optimal frequency ranges for detection are 3–5 GHz and 9 GHz. The thickness of the scalp impacts the detectability of skull fractures, yet fractures remain detectable even with the model with thicker scalp. These findings support the potential of microwave technique for developing portable, point-of-care devices for timely and precise skull fracture pre-diagnosis and frequent monitoring of the healing process. This approach particularly benefits young children, for whom conventional screening methods may pose more significant challenges.

Finnish Journal of eHealth and eWelfareVol. 17(1)
Oulu University Hospital (FI), Oulu University of Applied Sciences (FI), University of Oulu (FI)
Openalex Percentile: Top 22%
COVID-19 diagnosis using AI
1.36
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