Year: 2024
Type: article
Abstract: One of the most common shoulder injuries is the rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old and up to 62% in individuals aged 80 years and older. In this article, we present first a 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 as a result of finite element discretization of the forward problem with use of the domain decomposition method to accelerate the computations. We use a 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 with traditional imaging methods.
Source: Sensors
DOI: 10.3390/s24206663
Institutions: Université Côte d'Azur, Laboratoire d'Électronique, Antennes et Télécommunications, Laboratoire Jean-Alexandre Dieudonné, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Sorbonne Université, Laboratoire Jacques-Louis Lions, ALPINES: Algorithmes et outils parallèles pour des simulations numériques intégrées, Eindhoven University of Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4403455955