Management of sports injury treatment and radiological data analysis based on enhanced MRI image retrieval using autoencoder-based deep learning

The retrieval of magnetic resonance imaging (MRI) data holds paramount importance in clinical settings and sports medicine due to the limitations of conventional methods, such as slow speed, low accuracy, and limited learning capabilities. Enhancing this retrieval process is critical for advancing sports injury diagnostics and treatment outcomes. Overcoming these challenges is vital for improving healthcare practices and sports medicine methodologies. This study investigates the utilization of autoencoders in deep learning to efficiently retrieve MRI data from databases for sports injury diagnosis and treatment, with a focus on the model's ability to be trained with a small amount of labeled data. This research aims to enhance the MRI data retrieval process by leveraging autoencoders, showcasing the potential of deep learning technologies in sports injury diagnostics without the necessity of extensive labeled datasets for training. Findings have showcased the remarkable benefits of this approach for MRI data retrieval tasks, achieving an average accuracy of 99.09%. This signifies the exceptional performance of the technique within this specific domain, demonstrating its effectiveness and reliability in extracting MRI data. This innovative methodology can enhance the management of archival data and diagnostic capabilities of medical images in sports injury contexts, offering an efficient and dependable solution for MRI data retrieval. It not only facilitates rapid clinical diagnosis and sports medicine research but also proposes a convenient approach for medical image file management.