RESUMEN
Ultrasound-guided perineural hydrodissection (HD) is a novel technique that has been found to be effective in providing mechanical release of perineural adhesions and decompression of the nerve, reducing inflammation and edema and restoring its physiological function. It has a significant impact on chronic neuropathic pain (20 ± 4 weeks with VAS < 5 or VAS diminished by 2 points after the procedure). Carpal tunnel syndrome (CTS) is a common entrapment mononeuropathy, and its distribution is typically innervated by the median nerve. Patients with mild or moderate CTS may benefit from nonsurgical treatments or conservative therapies. This review was conducted following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement guidelines. Four investigators assessed each title, abstract, and full-text article for eligibility, with disagreements being resolved by consensus with two experienced investigators. The qualitative assessment of the studies was carried out using the modified Oxford quality scoring system, also known as the modified Jadad score. Furthermore, risk of possible biases was assessed using the Cochrane collaboration tool. The results of this review suggest that US-guided HD is an innovative, effective, well-tolerated, and safe technique (11 out of 923 patients had collateral or side effects after the procedure). However, further studies comparing all drugs and with a larger sample population are required to determine the most effective substance.
RESUMEN
The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model's effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively.
