ABSTRACT
BACKGROUND: Approximately 90% of patients who undergo arthroscopic rotator cuff repair (RCR) are satisfied with their pain levels and function after surgery. However, a subset of patients experience continued symptoms that warrant revision surgery. Preoperative risk factors for RCR failure requiring revision surgery have not been clearly defined. PURPOSE: To (1) determine the rate of RCR failure requiring revision surgery and (2) identify risk factors for revision surgery, which will help surgeons to determine patients who are at the greatest risk for RCR failure. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: A systematic review and meta-analysis in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were performed. The initial search resulted in 3158 titles, and 533 full-text articles were assessed for eligibility. A total of 10 studies met the following inclusion criteria: (1) human clinical studies, (2) arthroscopic RCR, (3) original clinical research, and (4) evaluation of preoperative risk factors for revision. RESULTS: After a full-text review, a total of 16 risk factors were recorded and analyzed across 10 studies. Corticosteroid injection was the most consistent risk factor for revision surgery, reaching statistical significance in 4 of 4 studies, followed by workers' compensation status (2/3 studies). Patients with corticosteroid injections had a pooled increased risk of revision surgery by 47% (odds ratio, 1.44 [95% CI, 1.36-1.52]). Patients with workers' compensation had a pooled increased risk of revision surgery by 133% (odds ratio, 2.33 [95% CI, 2.09-2.60]). Age, smoking status, diabetes, and obesity were found to be risk factors in half of the analyzed studies. CONCLUSION: Corticosteroid injections, regardless of the frequency of injections, and workers' compensation status were found to be significant risk factors across the literature based on qualitative analysis and pooled analysis. Surgeons should determine ideal candidates for arthroscopic RCR by accounting for corticosteroid injection history, regardless of the frequency, and insurance status of the patient.
ABSTRACT
Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including "artificial intelligence" in "radiologic examinations" of patients with "multiple myeloma". The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM.
