ObjectiveTo systematically review the efficacy of opioid-sparing analgesic techniques in terms of analgesic potential, incidence of complications and quality of recovery in liver surgery. MethodsThe PubMed, Embase and Cochrane Library databases were electronically searched to collect randomized controlled trials (RCTs) related to the objects from inception to August 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.3 software. ResultsA total of 20 RCTs involving 1 347 patients were included. The results of meta-analysis showed that opioid-sparing techniques could significantly reduce pain scores at rest and during movement from 2h to 48h postoperatively, opioid consumption within 24h (MD=−11.17, 95%CI −14.62 to −7.71, P<0.01) and 48h (MD=−7.19, 95%CI −10.06 to −4.33, P<0.01), postoperative nausea and vomiting (PONV) (OR=0.68, 95%CI 0.50 to 0.91, P=0.01) and wound infection (OR=0.42, 95%CI 0.18 to 0.98, P=0.04), as well as reduced time to bowel recovery (MD=−12.92, 95%CI −21.24 to −4.61, P<0.01) and decreased length of hospital stay (LOS) (MD=−0.90, 95%CI 1.32 to −0.49, P<0.01). No significant difference was observed between the two groups in the incidence of excessive sedation, pruritus, hypotension, headache and respiratory depression. Time to out-of-bed activity and patient satisfaction were also similar between groups. ConclusionOpioid-sparing techniques are effective in relieving postoperative pain and reducing opioid use, with additional potential in reducing postoperative nausea or vomiting, wound infection, time to bowel recovery and length of hospital stay.
Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.