ENHANCING DIAGNOSTIC ACCURACY IN SPORTS MEDICINE: A MACHINE LEARNING APPROACH TO IMAGE DENOISING FOR MUSCULOSKELETAL IMAGING

Abstract

Objective: This study investigates the application of machine learning (ML) techniques to image denoising, specifically in the enhancement of musculoskeletal imaging for sports medicine. Given the critical role of clear and accurate imaging in diagnosing sports-related injuries, improving image quality through advanced denoising methods is vital. Methods: The research focused on the development of a new machine learning-based image denoising framework, employing convolutional neural networks (CNNs) to refine the quality of musculoskeletal images. We compared this method against traditional linear filtering techniques to evaluate efficacy. The denoising process involved adjusting grayscale values and thresholds for each pixel to enhance the signal-to-noise ratio, thus reducing noise while maintaining the integrity of crucial anatomical details. Results: Experimental outcomes demonstrated that the ML-based method outperformed traditional techniques, effectively removing textural interference and accelerating the imaging process. The structural similarity (SSIM) index of the CNN-based denoising approach reached approximately 0.92, indicating a high fidelity compared to the original image and superior noise reduction capabilities. This improvement in image clarity is crucial for accurately identifying features such as fractures or soft tissue anomalies in sports-related injuries. Conclusion: Machine learning techniques, specifically CNNs, significantly enhance the quality of musculoskeletal imaging in sports medicine by reducing noise and improving the visual accuracy of diagnostic images. These advancements not only aid in the precise diagnosis of sports injuries but also contribute to the broader application of intelligent vision technology in medical imaging. The integration of ML in image processing promises substantial benefits in medical diagnostics, offering clearer insights into injury mechanisms and potentially improving treatment outcomes