RESUMO
Background and objective: Virtual reality is a promising pain control strategy for various pain conditions. This umbrella review of systematic reviews and meta-analyses aims to evaluate the analgesic effects of virtual reality. Methods: We searched for the relevant reviews in Scopus, PubMed and Cochrane library. Our primary outcome was pain, with secondary outcomes including disability, general health status, patient satisfaction, depression, balance, fear of movement, and adverse events. The quality of included articles was evaluated using the AMSTAR-2 tool. Results: 21 systematic reviews and meta-analyses with 274 studies and 17,680 patients were included in this review. All the reviews concluded benefits of virtual reality in managing pain conditions, including chronic and pain. Discussion and conclusions: This umbrella review demonstrates successful application of virtual reality in pain control, including perioperative, periprocedural, and chronic pain settings. Virtual reality can be used as an alternative therapy for pain management in children and adults.
RESUMO
Background: Regional anesthesia is increasingly used in acute postoperative pain management. Ultrasound has been used to facilitate the performance of the regional block, increase the percentage of successfully performed procedures and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to review the evidence on the application of artificial intelligence for optimization and interpretation of the sonographic image, and visualization of needle advancement and injection of local anesthetic. Methods: To conduct this scoping review, we followed the PRISMA-S guidelines. We included studies if they met the following criteria: (1) Application of Artificial intelligence-assisted in ultrasound-guided regional anesthesia; (2) Any human subject (of any age), object (manikin), or animal; (3) Study design: prospective, retrospective, RCTs; (4) Any method of regional anesthesia (epidural, spinal anesthesia, peripheral nerves); (5) Any anatomical localization of regional anesthesia (any nerve or plexus) (6) Any methods of artificial intelligence; (7) Settings: Any healthcare settings (Medical centers, hospitals, clinics, laboratories. Results: The systematic searches identified 78 citations. After the removal of the duplicates, 19 full-text articles were assessed; and 15 studies were eligible for inclusion in the review. Conclusions: AI solutions might be useful in anatomical landmark identification, reducing or even avoiding possible complications. AI-guided solutions can improve the optimization and interpretation of the sonographic image, visualization of needle advancement, and injection of local anesthetic. AI-guided solutions might improve the training process in UGRA. Although significant progress has been made in the application of AI-guided UGRA, randomized control trials are still missing.