All-arthroscopic supraspinatus and infraspinatus muscle advancement leads to high healing rate and excellent outcomes in patients with massive, retracted rotator cuff tears, even in patients with pseudoparalysis.

PURPOSE: The purpose of this study was to evaluate the clinical and radiographic outcomes of an all-arthroscopic rotator cuff repair technique involving muscle advancement and double-layer lasso loop (DLLL) repair for massive, retracted posterosuperior cuff tears. METHODS: This is a retrospective case series of patients with massive, retracted posterosuperior cuff tears who underwent the all-arthroscopic muscle advancement technique from March 2017 to September 2021, with minimum follow-up of 12 months. Key steps included suprascapular nerve release, advancement of supraspinatus and infraspinatus muscles, and DLLL repair. Preoperative and postoperative visual analogue scale for pain, American Shoulder and Elbow Surgeons (ASES) score, Constant score, University of California Los Angeles (UCLA) shoulder score, active range of motion (ROM), and strength were compared. Preoperative and postoperative structural radiological characteristics were analysed. RESULTS: Forty-three shoulders in 38 patients were evaluated with mean follow-up of 18.8 months (range 12-55 months). Of the 43 shoulders, 4 repairs failed (9.3% retear rate). VAS, ASES, Constant, and UCLA scores significantly improved (p<0.001) in patients who demonstrated healing on postoperative MRI (n=39). ASES, Constant, and UCLA scores were significantly better in the healed group, with 100% exceeding MCIDs for ASES and UCLA scores, and 84.2% for Constant score. A lower proportion of patients in the retear group achieved MCIDs. Active ROM in all planes significantly improved for those who had healed repairs. (p<0.001). Relative strengths of abduction, supraspinatus, and infraspinatus were at least 90% of the contralateral side. Recovery rate of pseudoparalysis (7 patients) was 100%. CONCLUSION: All-arthroscopic muscle advancement, coupled with double-layer lasso loop repair, leads to a high healing rate with excellent clinical outcomes and recovery of strength to at least 90%, even in patients with pseudoparalysis.

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Does forearm referencing using a retroversion guide achieve the targeted retroversion of the humeral component in reverse shoulder arthroplasty?

BACKGROUND: Component positioning affects clinical outcomes of reverse shoulder arthroplasty, which necessitates an implantation technique that is reproducible, consistent, and reliable. This study aims to assess the accuracy and precision of positioning the humeral component in planned retroversion using a forearm referencing guide. METHODS: Computed tomography scans of 54 patients (27 males and 27 females) who underwent primary reverse shoulder arthroplasty for osteoarthritis or cuff tear arthropathy were evaluated. A standardized surgical technique was used to place the humeral stem in 15° of retroversion. Version was assessed intraoperatively visualizing the retroversion guide from above and referencing the forearm axis. Metal subtraction techniques from postoperative computed tomography images allowed for the generation of 3D models of the humerus and for evaluation of the humeral component position. Anatomical humeral plane and implant planes were defined and the retroversion 3D angle between identified planes was recorded for each patient. Accuracy and precision were assessed. A subgroup analysis evaluated differences between male and female patients. RESULTS: The humeral retroversion angle ranged from 0.9° to 22.8°. The majority (81%) of the measurements were less than 15°. Mean retroversion angle (±SD) was 9.9° ± 5.8° (95% CI 8.4°-11.5°) with a mean percent error with respect to 15° of -34% ± 38 (95% CI -23 to -44). In the male subgroup (n = 27, range 3.8°-22.5°), the mean retroversion angle was 11.9° ± 5.4° (95% CI 9.8°-14.1°) with a mean percent error with respect to 15° of -21% ± 36 (95% CI -6 to -35). In the female subgroup (n = 27, range 0.9°-22.8°), mean retroversion angle was 8.0° ± 5.5° (95% CI 5.8°-10.1°) and the mean percent error with respect to 15° was -47% ± 36 (95% CI -32 to -61). The differences between the 2 gender groups were statistically significant (P = .006). CONCLUSION: Referencing the forearm using an extramedullary forearm referencing system to position the humeral stem in a desired retroversion is neither accurate nor precise. There is a nonnegligible tendency to achieve a lower retroversion than planned, and the error is more marked in females.

Outcomes and survivorship of anatomic total shoulder arthroplasty: current concepts.

Total shoulder arthroplasty (TSA) has been the gold standard of care for end-stage glenohumeral arthritis. Outcomes are varied and have been affected by both patient and implant characteristics. Patient factors, such as age, preoperative diagnosis, and preoperative glenoid morphology, can affect the outcomes after TSA. Similarly, the different glenoid and humeral component designs significantly affect the survivorship of TSA. Significant evolution has occurred in the design of the glenoid component with the aim of decreasing the glenoid-sided causes of failure in TSA. On the other hand, focus on the humeral component has been increasing as well, with a trend towards using shorter humeral stems. This article aims to look at the outcomes of TSA as affected by the various patient characteristics and design options for the glenoid and the humeral components. This review also aims to compare survivorship data from global literature and the Australian joint replacement registry and to provide insights into the implant combination that may provide the best patient outcome.

Single-Stage Revision Reverse Shoulder Arthroplasty: Preoperative Planning, Surgical Technique, and Mixed Reality Execution.

Revision shoulder arthroplasty is increasing with the number of primary shoulder replacements rising globally. Complex primary and revisions of shoulder arthroplasties pose specific challenges for the surgeon, which must be addressed preoperatively and intraoperatively. This article aimed to present strategies for the management of revision of shoulder arthroplasties through a single-stage approach. Preoperatively, patient factors, such as age, comorbidities, and bone quality, should be considered. The use of planning software can aid in accurately evaluating implants in situ and predict bony anatomy that will remain after explantation during the revision surgery. The planning from such software can then be executed with the help of mixed reality technology to allow accurate implant placement. Single-stage revision is performed in two steps (debridement as first step, implantation and reconstruction as the second step), guided by the following principles: adequate debridement while preserving key soft tissue attachments (i.e., rotator cuff, pectoralis major, latissimus dorsi, deltoid), restoration of glenoid joint line using bone grafting, restoration of humeral length, reconstruction and/or reattachment of soft tissues, and strict compliance with the postoperative antibiotic regimen. Preliminary results of single-stage revision shoulder arthroplasty show improvement in patient outcomes (mean 1 year), successful treatment of infection for those diagnosed with periprosthetic joint infection, and improved cost-benefit parameters for the healthcare system.