Monitoring bone healing: Integrating RF sensing with AI
Aldelemy, Ahmad Adjei, E. Siaw, P.O. Al-Dulaimi, A. Ali, N.T. Twigg, Peter C.
Aldelemy, Ahmad
Adjei, E.
Siaw, P.O.
Al-Dulaimi, A.
Ali, N.T.
Twigg, Peter C.
Publication Date
2025-01
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2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.
For more information, see https://creativecommons.org/licenses/by/4.0/
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2024-12-18
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Abstract
This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diameter, muscle thickness, fat thickness, and hematoma size, augmented with multiple sensor configurations (two, four, six, and eight sensors). The model aims to provide a frequent, non-invasive assessment of the fracture healing process compared to conventional imaging methods. Our approach leverages data from six RF sensors, achieving a high overall accuracy of 99.2% in classifying different fracture stages, including “no fracture” and varying degrees of hematoma sizes. The findings indicate that increasing the number of sensors up to six significantly enhances detection accuracy and sensitivity across all fracture stages. However, the marginal improvement from six to eight sensors was not statistically significant, suggesting that a six-sensor configuration offers an optimal balance between performance and system complexity. The results demonstrate significant potential for this technology to revolutionise orthopaedic treatment and recovery management by offering continuous, real-time monitoring without radiation exposure. The proposed system enhances personalised patient care by integrating RF sensing with artificial intelligence, enabling timely interventions and more informed, data-driven treatment strategies. This research lays a robust foundation for future advancements, including three-dimensional modelling and clinical validations, toward the practical implementation of non-invasive fracture monitoring systems.
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Published version
Citation
Aldelemy A, Adjei E, Siaw PO et al (2025) Monitoring bone healing: Integrating RF sensing with AI. IEEE Access. 13: 11114-11135.
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Article