Utility of Platelet-Rich Plasma Therapy in the Management of Meniscus Injuries: A narrative review.

Menisci are crucial for knee joint functions and meniscal tears are common injuries, especially in sports activities. Platelet-rich plasma (PRP), which enhances healing, has emerged as a promising additive treatment for meniscus injuries, utilizing the regenerative properties of platelets and growth factors for improved clinical outcomes. In studies with a follow-up period of less than one year, the use of platelet-rich plasma (PRP) therapy for meniscus injuries showed significant improvements in knee symptoms and daily activity. Patients experienced enhanced outcomes in terms of pain reduction and increased sports activity, with MRI scans indicating stable meniscus conditions after six months. Studies with a follow-up of more than one year, however, did not find significant differences between groups treated with PRP and groups not treated with PRP in terms of various outcome measures, including pain and knee function. The vascularization of the menisci is vital for their proper function, and insufficient blood supply can affect healing of meniscal injuries. PRP therapy is used to enhance meniscal healing by introducing growth factors and anti-inflammatory agents. PRP therapy may enable athletes with meniscal tears to return to sports more quickly and has less rehabilitation duration. While PRP seems promising as an alternative to failed treatment or as an adjunct to treatment in the short term, its long-term effectiveness remains inconclusive. Patient preferences, commitment to therapy rehabilitation, and cost should all be considered on an individual basis.

Creating realistic anterior segment optical coherence tomography images using generative adversarial networks.

AIMS: To develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images. METHODS: This study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images. To assess the suitability of the generated images for machine learning tasks, a convolutional neural network (CNN) was trained using a dataset of real and generated images over a classification task. The generated AS-OCT images were then upsampled using an enhanced super-resolution GAN (ESRGAN) to achieve high resolution. RESULTS: The generated images exhibited visual and quantitative similarity to real AS-OCT images. Quantitative similarity assessed using FID scored an average of 6.32. Surgeons scored 51.7% in identifying real versus generated images which was not significantly better than chance (p value >0.3). The CNN accuracy improved from 78% to 100% when synthetic images were added to the dataset. The ESRGAN upsampled images were objectively more realistic and accurate compared with traditional upsampling techniques by scoring a lower Learned Perceptual Image Patch Similarity of 0.0905 compared with 0.4244 of bicubic interpolation. CONCLUSIONS: This study successfully developed and leveraged GANs capable of generating high-definition synthetic AS-OCT images that are realistic and suitable for machine learning and image analysis tasks.