No difference in biomechanical properties of simple, horizontal mattress, and double row repair in Bankart repair: a systematic review and meta-analysis of biomechanical studies.

BACKGROUND: Arthroscopic Bankart repair is the most common procedure in patients with anterior shoulder instability. Various repair techniques using suture anchors have been used to improve the strength of fixation and surgical outcomes in arthroscopic Bankart surgery. However, evidence regarding which method is superior is lacking. This systematic review and meta-analysis study was designed to compare the biomechanical results of simple versus horizontal mattress versus double-row mattress for Bankart repair. METHODS: A systematic search of the MEDLINE, Embase, and Cochrane Library databases was performed to identify comparative biomechanical studies comparing the simple, horizontal mattress, and double-row techniques commonly used in Bankart repair for anterior shoulder instability. Biomechanical results included the ultimate load to failure, stiffness, cyclic displacement, and mode of failure after the ultimate load. The methodological quality was assessed based on the Quality Appraisal for Cadaveric Studies (QUACS) scale for biomechanical studies. RESULTS: Six biomechanical studies comprising 125 human cadavers were included in this systematic review. In biomechanical studies comparing simple and horizontal mattress repair and biomechanical studies comparing simple and double-row repair, there were no significant differences in the ultimate load to failure, stiffness, or cyclic displacement between the repair methods. The median QUACS scale was 11.5 with a range from 10 to 12, indicating a low risk of bias. CONCLUSION: There was no biomechanically significant difference between the simple, horizontal mattress, and double-row methods in Bankart repair. Clinical evidence such as prospective randomized controlled trials should be conducted to evaluate clinical outcomes according to the various repair methods. LEVEL OF EVIDENCE: Systematic review, Therapeutic level IV.

Linear stratified approach using full geometric constraints for 3D scene reconstruction and camera calibration.

This paper presents a new linear framework to obtain 3D scene reconstruction and camera calibration simultaneously from uncalibrated images using scene geometry. Our strategy uses the constraints of parallelism, coplanarity, colinearity, and orthogonality. These constraints can be obtained in general man-made scenes frequently. This approach can give more stable results with fewer images and allow us to gain the results with only linear operations. In this paper, it is shown that all the geometric constraints used in the previous works performed independently up to now can be implemented easily in the proposed linear method. The study on the situations that cannot be dealt with by the previous approaches is also presented and it is shown that the proposed method being able to handle the cases is more flexible in use. The proposed method uses a stratified approach, in which affine reconstruction is performed first and then metric reconstruction. In this procedure, the additional constraints newly extracted in this paper have an important role for affine reconstruction in practical situations.

Convenient calibration method for unsynchronized camera networks using an inaccurate small reference object.

In this paper, a new and convenient calibration algorithm is proposed for unsynchronized camera networks with a large capture volume. The proposed method provides a simple and accurate means of calibration using a small 3D reference object. Moreover, since the inaccuracy of the object is also compensated simultaneously, the manufacturing cost can be decreased. The extrinsic and intrinsic parameters are recovered simultaneously by capturing an object placed arbitrarily in different locations in the capture volume. The proposed method first resolves the problem linearly by factorizing projection matrices into the camera and the object pose parameters. Due to the multi-view constraints imposed on factorization, consistency of the rigid transformations among cameras and objects can be imposed. These consistent estimation results can be further refined using a non-linear optimization process. The proposed algorithm is evaluated via simulated and real experiments in order to verify that it is more efficient than previous methods.