Navigated augmented reality through a head-mounted display leads to low deviation between planned, intra- and postoperative parameters during glenoid component placement of reverse shoulder arthroplasty: a proof-of-concept case series.

Background: Navigated augmented reality (AR) through a head-mounted display (HMD) has led to accurate glenoid component placement in reverse shoulder arthroplasty (RSA) in an in-vitro setting. The purpose of this study is to evaluate the deviation between planned, intraoperative, and postoperative inclination, retroversion, entry point, and depth of the glenoid component placement during RSA, assisted by navigated AR through an HMD, in a surgical setting.

Methods: A prospective, multicenter study was conducted.

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction.

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification.

Peri-Prosthetic Joint Infection of the Knee Causes High Levels of Psychosocial Distress: A Prospective Cohort Study.

Peri-prosthetic joint infection (PJI) is a major complication of knee arthroplasty that can cause long-term disability. In addition to its physical impact, there is a clear psychological burden that has not been measured yet. We hypothesized that the psychosocial burden of PJI can be assessed quantitatively using standardized questionnaires and may be correlated with treatment stage.