ABSTRACT
Introduction: Osteoarthritis (OA) is the most common form of arthritis and can severely affect function and quality of life. Platelet-rich plasma (PRP) is derived from a patient's own blood and has potential as an adjunct to treat OA. However, research has been limited for small joints such as the carpometacarpal (CMC) joint. Clinical Findings: A 65-year-old Caucasian male complained of bilateral wrist and neck pain after a motor vehicle accident. His initial exam noted swelling, tenderness and pain with movement at the bilateral thumb CMC joints and anatomic snuffboxes. However, there was no strength, range of motion or tactile deficits during examination. Outcomes: He was found to have moderate to severe OA bilaterally in his left and right CMC joints seen on computed tomography and magnetic resonance imaging. The patient was initially treated with ultrasound (US)-guided steroid injections but did not experience significant improvement. After careful discussion, the patient chose to undergo US-guided injection of PRP into both joints. Follow-up at six weeks after PRP injection revealed that there was functional improvement in both joints as well as objective improvement via the Mayo Wrist Score survey. Conclusion: US-guided PRP injection can be used as an alternative modality to treat OA of the CMC joints when approaches such as conservative therapy and steroid injections have failed. PRP has not been as well studied as other interventions such as corticosteroid injections, but it may offer less long-term adverse effects and be considered a potential alternative or adjuvant to current treatment modalities.
ABSTRACT
Mixed sample augmentation (MSA) has witnessed great success in the research area of semi-supervised learning (SSL) and is performed by mixing two training samples as an augmentation strategy to effectively smooth the training space. Following the insights on the efficacy of cut-mix in particular, we propose FMixCut, an MSA that combines Fourier space-based data mixing (FMix) and the proposed Fourier space-based data cutting (FCut) for labeled and unlabeled data augmentation. Specifically, for the SSL task, our approach first generates soft pseudo-labels using the model's previous predictions. The model is then trained to penalize the outputs of the FMix-generated samples so that they are consistent with their mixed soft pseudo-labels. In addition, we propose to use FCut, a new Cutout-based data augmentation strategy that adopts the two masked sample pairs from FMix for weighted cross-entropy minimization. Furthermore, by implementing two regularization techniques, namely, batch label distribution entropy maximization and sample confidence entropy minimization, we further boost the training efficiency. Finally, we introduce a dynamic labeled-unlabeled data mixing (DDM) strategy to further accelerate the convergence of the model. Combining the above process, we finally call our SSL approach as "FMixCutMatch", in short FMCmatch. As a result, the proposed FMCmatch achieves state-of-the-art performance on CIFAR-10/100, SVHN and Mini-Imagenet across a variety of SSL conditions with the CNN-13, WRN-28-2 and ResNet-18 networks. In particular, our method achieves a 4.54% test error on CIFAR-10 with 4K labels under the CNN-13 and a 41.25% Top-1 test error on Mini-Imagenet with 10K labels under the ResNet-18. Our codes for reproducing these results are publicly available at https://github.com/biuyq/FMixCutMatch.
