Outcomes Following Revision Reverse Shoulder Arthroplasty for Infection.

BACKGROUND: In comparison to primary reverse shoulder arthroplasty (RSA) procedures, revision arthroplasty can be a longer and more complex procedure leading to an increased risk of complications. The reported rates of infection in primary RSA range from 1% to 19% and the cost impact on patients and healthcare systems is significant, leading to multiple revision surgeries. The purpose of this study was to evaluate the postoperative outcomes, complications, and revision rates for revision reverse shoulder arthroplasty (rRSA) due to infection compared to rRSA for non-infectious causes. METHODS: Patients who underwent rRSA between 2009 and 2020 by a single fellowship-trained orthopedic surgeon at a single institution were retrospectively identified through a prospectively collected database. Patients were separated into two cohorts based on revision diagnosis: (1) rRSA due to infection (rRSAi), and (2) rRSA due to non-infectious causes (rRSAn). Patient-reported outcome scores (PROs), including the Simple Shoulder Test (SST), Constant score, American Shoulder and Elbow Surgeons (ASES) score, University of California-Los Angeles (UCLA), Shoulder Arthroplasty Smart score (SAS), and active range of motion (ROM) were collected preoperatively and at a minimum one year follow-up. Postoperative complications and revision rates were also collected. RESULTS: A total of 93 patients (n=19 rRSAi group, n=74 rRSAn group) with a mean age of 68 years were included in this analysis. All baseline demographics were comparable between groups. No significant differences were found in preoperative or postoperative PROs and ROM between the two groups. Postoperative complication rates and revision rates were comparable between the groups. CONCLUSION: Revision reverse shoulder arthroplasty due to infection results in similar patient-reported outcome scores, range of motion, and revision rates when compared to rRSA for non-infectious causes. Our results suggest that despite the unique challenges associated with rRSA for infection, patient outcomes do not differ from cases attributed to non-infectious causes. Further efforts are warranted to further validate and contextualize these findings, considering the protentional influence of patient-specific and implant-specific factors.

Artificial Intelligence and Machine Learning in Rotator Cuff Tears.

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.

Focused ultrasound for treatment of peripheral brain tumors.

Malignant brain tumors are the leading cause of cancer-related death in children and remain a significant cause of morbidity and mortality throughout all demographics. Central nervous system (CNS) tumors are classically treated with surgical resection and radiotherapy in addition to adjuvant chemotherapy. However, the therapeutic efficacy of chemotherapeutic agents is limited due to the blood-brain barrier (BBB). Magnetic resonance guided focused ultrasound (MRgFUS) is a new and promising intervention for CNS tumors, which has shown success in preclinical trials. High-intensity focused ultrasound (HIFU) has the capacity to serve as a direct therapeutic agent in the form of thermoablation and mechanical destruction of the tumor. Low-intensity focused ultrasound (LIFU) has been shown to disrupt the BBB and enhance the uptake of therapeutic agents in the brain and CNS. The authors present a review of MRgFUS in the treatment of CNS tumors. This treatment method has shown promising results in preclinical trials including minimal adverse effects, increased infiltration of the therapeutic agents into the CNS, decreased tumor progression, and improved survival rates.