Version 1
: Received: 30 August 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (10:57:56 CEST)
How to cite:
Borzooei, S.; Tournier, P.-H.; Dolean, V.; Migliaccio, C. Microwave Digital Twin Prototype for the Shoulder Injury Detection. Preprints2024, 2024090070. https://doi.org/10.20944/preprints202409.0070.v1
Borzooei, S.; Tournier, P.-H.; Dolean, V.; Migliaccio, C. Microwave Digital Twin Prototype for the Shoulder Injury Detection. Preprints 2024, 2024090070. https://doi.org/10.20944/preprints202409.0070.v1
Borzooei, S.; Tournier, P.-H.; Dolean, V.; Migliaccio, C. Microwave Digital Twin Prototype for the Shoulder Injury Detection. Preprints2024, 2024090070. https://doi.org/10.20944/preprints202409.0070.v1
APA Style
Borzooei, S., Tournier, P. H., Dolean, V., & Migliaccio, C. (2024). Microwave Digital Twin Prototype for the Shoulder Injury Detection. Preprints. https://doi.org/10.20944/preprints202409.0070.v1
Chicago/Turabian Style
Borzooei, S., Victorita Dolean and Claire Migliaccio. 2024 "Microwave Digital Twin Prototype for the Shoulder Injury Detection" Preprints. https://doi.org/10.20944/preprints202409.0070.v1
Abstract
One of the most common shoulder injuries is rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old to 62% in individuals aged 80 and older. In this article, we present first Microwave Digital Twin Prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling, in order to bypass real-world data collection challenges. This involves solving the linear system in result of finite element discretization of the forward problem with use of domain decomposition method to accelerate the computations. We use support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared to the traditional imaging methods.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
