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Purpose: To systematically review the literature to evaluate the clinical studies on bioinductive collagen implant (BCI) for the treatment of rotator cuff tears., Methods: A literature search of MEDLINE, Embase, and the Cochrane Library was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Clinical studies reporting BCI for rotator cuff tears were included. Quantitive and qualitative data were evaluated., Results: A total of 21 studies were included. In patients with full-thickness tears, 7 of the 8 studies with pre- to postoperative American Shoulder and Elbow Surgeons (ASES) scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 75% to 100% of patients meeting the minimal clinically important difference. In those with partial-thickness tears, 7 of the 8 studies with pre- to postoperative ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 54.4% to 100% of patients meeting the minimal clinically important difference. For studies that quantified percent increases in tendon thickness, the reported increases ranged from 13% to 44% in full-thickness tears and 14% to 60% in partial-thickness tears. Six studies evaluated rotator cuff retears after BCI treatment in the full-thickness cohort, with rates reported ranging from 0% to 9%. Five studies evaluated rotator cuff retears after BCI treatment in the partial-thickness cohort, with rates reported ranging from 0% to 18%. Two of the included studies found that BCI was cost-effective due to the increased tendon healing, with cost savings of $5,338 to $13,061 per healed rotator cuff tendon., Conclusions: The literature on rotator cuff tear augmentation with BCI has shown consistently reported good results. Additionally, there was evidence of low retear rates and consistently improved tendon thickness with BCI, with 2 randomized controlled trials showing improved tendon healing with BCI. However, there appears to be a higher rate of adhesive capsulitis reported., Level of Evidence: Level IV, systematic review of Level I, III, and IV studies., (Copyright © 2024 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

Background: Reverse shoulder arthroplasty (RSA) is an established operative treatment for failed rotator cuff repair (RCR) that may not be amenable to revision repair. The purpose of this meta-analysis is to evaluate the clinical outcomes for patients undergoing RSA following prior failed RCR compared with patients without prior RCR undergoing primary RSA. Methods: A systematic search of articles in Pubmed, EMBASE and The Cochrane Library databases was carried out according to PRISMA guidelines. Comparative studies assessing outcomes of RSA after failed RCR versus primary RSA were included. Results: Seven studies with 2149 total patients were included; 760 patients had a prior RCR and 1389 patients did not have a prior RCR. Prior failed RCR resulted in significantly lower postoperative ASES scores (mean difference [MD], −8.31 95% confidence interval [CI] −10.96, −5.66), less forward flexion (MD, −6.71 95%CI −11.75, −1.67), and higher VAS pain scores (MD; 0.85, 95% CI 0.47, 1.22) when compared to primary RSA. There were no significant differences in external rotation, complications rate, or rate of revision. Conclusion: This study found that failed RCR prior to RSA was associated with lower functional outcomes scores, higher pain scores, and worse range of motion compared to primary RSA without prior RCR. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery. [ABSTRACT FROM AUTHOR]