The glenohumeral joint is the most dislocated articulation, accounting for more than 50% of all joint dislocations. The reason behind shoulder instability should be investigated in detail for successful management, and the treatment plan should be individualized for all patients. Several classification systems have been proposed for glenohumeral instability. A physical exam is mandatory no matter what classification system is used. When treating patients with anterior shoulder instability, surgeons need to be aware of the critical size of the bone loss, which is commonly seen. The glenoid track concept was clinically adopted, and the measurement of the glenoid track for surgical decision-making is recommended. Detailed assessment of existing soft tissue injury to the labrum, capsule, glenohumeral ligaments, and rotator cuff is also mandatory as their presence influences the surgical outcome. Rehabilitation, arthroscopic repair techniques, open Bankart procedure, capsular plication, remplissage, Latarjet technique, iliac crest, and other bone grafts offer the surgeon different treatment options according to the type of patient and the lesions to be treated. Three-dimensional (3D) technologies can help to evaluate glenoid and humeral defects. Patient-specific guides are low-cost surgical instruments and can be used in shoulder instability surgery. 3D printing will undoubtedly become an essential tool to achieve the best results in glenohumeral instability surgery.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2–3 years, and 3–5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.