Abstract
There are a lot of potential causes of shoulder fractures due to the joint's greater mobility compared to others in the body. To identify these breaks, doctors use data from imaging modalities like as computed tomography (CT), MRI, or X-rays. The goal of this study is to help clinicians by developing a system that can use quantum photonics and artificial intelligence (AI) to determine whether an X-ray of the shoulder shows a fracture or not. This study uses machine learning photonics and quantum computing to evaluate joint bone dislocation in athletes. Regression-based pulse convolutional segNet architecture with nanophotonic analysis (RPCSeg_NP) is used in this investigation to examine the bone dislocation. The validation accuracy, sensitivity, positive predictive value, and similarity index (SSIM) are measured throughout the experimental study. Artificial intelligence has the ability to precisely identify and categorise proximal humerus fractures on standard shoulder AP radiographs. To find out if using artificial intelligence in the clinic is feasible and if it can enhance patient care and results in comparison to existing orthopaedic evaluations, more research is required.
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Xu, Y. Joint bone dislocation analysis for athlete player using quantum photonics in healthcare and sports application. Opt Quant Electron 56, 447 (2024). https://doi.org/10.1007/s11082-023-06096-7
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DOI: https://doi.org/10.1007/s11082-023-06096-7