Reverse shoulder arthroplasty is an ideal treatment for glenohumeral dysfunction due to cuff tear arthropathy. As the number of patients treated with reverse shoulder arthroplasty is increasing, the incidence of complications after this procedure also is increasing. The rate of complications in reverse shoulder arthroplasty was reported to be 15%–24%. Recently, the following complications have been reported in order of frequency: periprosthetic infection, dislocation, periprosthetic fracture, neurologic injury, scapular notching, acromion or scapular spine fracture, and aseptic loosening of prosthesis. However, the overall complication rate has varied across studies because of different prosthesis used, improvement of implant and surgical skills, and different definitions of complications. Some authors included complications that affect the clinical outcomes of the surgery, while others reported minor complications that do not affect the clinical outcomes such as minor reversible neurologic deficit or minimal scapular notching. This review article summarizes the processes related to diagnosis and treatment of complications after reverse shoulder arthroplasty with the aim of helping clinicians reduce complications and perform appropriate procedures if/when complications occur.
Purpose This study aimed to compare the results of using knotless and knot-tying suture anchors in arthroscopic Bankart repair. Materials and Methods The patients who underwent arthroscopic Bankart repair between 2011 and 2017 using knot-tying and knotless suture anchors were retrospectively reviewed. We collected demographic data, clinical scores (pain visual analogue scale), functional visual analogue scale, American Shoulder and Elbow Society scores, and Rowe score), and range of motion (ROM). Re-dislocation and subjective anterior apprehension test rates between the two techniques were also analyzed. Results Of the 154 patients who underwent arthroscopic Bankart repair, 115 patients (knot-tying group: n=61 and knotless group: n=54) were included in this study. Of the 115 patients, 102 were male and 13 were female. The mean patient age was 27 years (range: 17–60), and the mean follow-up period was 43 months (range: 24–99). There were no significant differences in the final clinical scores and ROM between the two groups. Re-dislocation was observed in 6 (9.8%) and 4 (7.3%) patients in the knot-tying and knotless groups, respectively. Apprehension was observed in 11 (18.0%) and 12 (22.2%) patients in the knot-tying and knotless groups, respectively. There were no significant differences between the two groups in regards to re-dislocation and anterior apprehension. Conclusion Re-dislocation rates and clinical scores were similar with the use of knotless and knot-tying suture anchors in arthroscopic Bankart repair after a minimal 2 year follow-up.
Purpose Early detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs. Methods Standard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors. Results The area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P<0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively. Conclusions The AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.
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