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Objective Rotator cuff tear is a common shoulder injury that often leads to serious limitations in daily life. Herein, a network Meta-analysis using frequency theory was performed to evaluate the clinical outcomes of five rotator cuff repair techniques, including single-row repair, double-row repair, suture bridge repair, platelet-rich plasma therapy, and bone marrow stimulation, thus guiding clinical decision-making on rotator cuff repair. Methods PubMed, EMbase, The Cochrane Library, and Web of Science were searched for randomized controlled trials and cohort studies comparing rotator cuff repair techniques published from inception to May 2022. Combined analysis and quality assessment were performed using software STATA15.1 and Review Manager5.3. Results A total of 51 articles were finally included, including 27 randomized controlled trials and 24 cohort studies. Results from the network Meta-analysis showed that: (1) In terms of the American Shoulder and Elbow Surgeons score, platelet-rich plasma therapy, double-row repair, bone marrow stimulation, and single-row repair were significantly better than suture bridge repair. (2) In terms of Constant score, bone marrow stimulation was significantly better than double-row repair, single-row repair, and suture bridge repair. (3) In terms of visual analog scale score, platelet-rich plasma therapy was significantly better than double-row repair and suture bridge repair. (4) In terms of the Shoulder Rating Scale of the University of California at Los Angeles score, platelet-rich plasma therapy and double-row repair were relatively better but not significantly different from the other treatments. (5) In terms of the risk of re-tear, the re-tear rate of platelet-rich plasma therapy and double-row repair was significantly lower than that of single-row repair and suture bridge repair. Conclusion Based on the results of network Meta-analysis and surface under the cumulative ranking, platelet-rich plasma therapy, bone marrow stimulation, and double-row repair have good overall rehabilitation effects. It is recommended to choose appropriate repair techniques as per the actual clinical situation.

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Abstract. Classic meanshift algorithm is widely used in pattern recognition, but its accuracy decrease large when there is perturbation in background. In this paper, to solve this problem, we put forward an improved meanshift algorithm which is based on self-adaption weighting meanshift algorithm. At first, we catch moving object area. Secondly, we use capture to set new weighting coefficient. Then we track target model by the new coefficient to decrease effect of background. The experiment result of moving body in supervisory control video shows perturbation insensitivity, robustness and stability.