Glenoid bony morphology of osteoarthritis prior to shoulder arthroplasty: what the surgeon wants to know and why

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans

Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumeral (GH) joint functionality, requiring a targeted treatment. Various options are available to restore joint functions, ranging from conservative management to surgical interventions, depending on the severity of the condition. This work introduces an innovative deep learning framework to process shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the GH joint region, and the staging of three common osteoarthritic-related conditions: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). Each condition was stratified into multiple severity stages, offering a comprehensive analysis of shoulder bone structure pathology. The pipeline comprised two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22 mm and 1.48 mm for the humerus and 0.24 mm and 1.48 mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the entire inference pipeline was less than 15 s, showcasing the framework's efficiency and compatibility with orthopedic radiology practice. The achieved reconstruction and classification accuracy, combined with the rapid processing time, represent a promising advancement towards the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline, delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.

Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning

Goal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty.

Prior fragility fractures are associated with a higher risk of 8-year complications following total shoulder arthroplasty

Patients who sustain fragility fractures prior to total shoulder arthroplasty have significantly higher risk for bone health-related complications within 8 years of procedure. Identification of these high-risk patients with an emphasis on preoperative, intraoperative, and postoperative bone health optimization may help minimize these preventable complications.

PURPOSE

As the population ages, more patients with osteoporosis are undergoing total shoulder arthroplasty (TSA), including those who have sustained a prior fragility fracture. Sustaining a fragility fracture before TSA has been associated with increased risk of short-term revision rates, periprosthetic fracture (PPF), and secondary fragility fractures but long-term implant survivorship in this patient population is unknown. Therefore, the purpose of this study was to characterize the association of prior fragility fractures with 8-year risks of revision TSA, periprosthetic fracture, and secondary fragility fracture.

METHODS

Patients aged 50 years and older who underwent TSA were identified in a large national database. Patients were stratified based on whether they sustained a fragility fracture within 3 years prior to TSA. Patients who had a prior fragility fracture (7631) were matched 1:1 to patients who did not based on age, gender, Charlson Comorbidity Index (CCI), smoking, obesity, diabetes mellitus, and alcohol use. Kaplan-Meier and Cox Proportional Hazards analyses were used to observe the cumulative incidences of all-cause revision, periprosthetic fracture, and secondary fragility fracture within 8 years of index surgery.

RESULTS

The 8-year cumulative incidence of revision TSA (5.7% vs. 4.1%), periprosthetic fracture (3.8% vs. 1.4%), and secondary fragility fracture (46.5% vs. 10.1%) were significantly higher for those who had a prior fragility fracture when compared to those who did not. On multivariable analysis, a prior fragility fracture was associated with higher risks of revision (hazard ratio [HR], 1.48; 95% confidence interval [CI], 1.24-1.74; p < 0.001), periprosthetic fracture (HR, 2.98; 95% CI, 2.18-4.07; p < 0.001) and secondary fragility fracture (HR, 8.39; 95% CI, 7.62-9.24; p < 0.001).

CONCLUSIONS

Prior fragility fracture was a significant risk factor for revision, periprosthetic fracture, and secondary fragility fracture within 8 years of primary TSA. Identification of these high-risk patients with an emphasis on preoperative and postoperative bone health optimization may help minimize these complications.

LEVEL OF EVIDENCE

III.

Update on Shoulder Arthroplasties with Emphasis on Imaging

Shoulder pain and dysfunction may significantly impact quality of life. If conservative measures fail, advanced disease is frequently treated with shoulder arthroplasty, which is currently the third most common joint replacement surgery following the hip and knee. The main indications for shoulder arthroplasty include primary osteoarthritis, post-traumatic arthritis, inflammatory arthritis, osteonecrosis, proximal humeral fracture sequelae, severely dislocated proximal humeral fractures, and advanced rotator cuff disease. Several types of anatomic arthroplasties are available, such as humeral head resurfacing and hemiarthroplasties, as well as total anatomic arthroplasties. Reverse total shoulder arthroplasties, which reverse the normal ball-and-socket geometry of the shoulder, are also available. Each of these arthroplasty types has specific indications and unique complications in addition to general hardware-related or surgery-related complications. Imaging-including radiography, ultrasonography, computed tomography, magnetic resonance imaging, and, occasionally, nuclear medicine imaging-has a key role in the initial pre-operative evaluation for shoulder arthroplasty, as well as in post-surgical follow-up. This review paper aims to discuss important pre-operative imaging considerations, including rotator cuff evaluation, glenoid morphology, and glenoid version, as well as to review post-operative imaging of the various types of shoulder arthroplasties, to include normal post-operative appearances as well as imaging findings of complications.

Variability and reliability of 2-dimensional vs. 3-dimensional glenoid version measurements with 3-dimensional preoperative planning software

BACKGROUND

Preoperative planning for total shoulder arthroplasty (TSA) may change according to the measured degree of glenoid version. Both 2-dimensional (2D) and 3-dimensional (3D) computed tomographic (CT) scans are used to measure glenoid version, with no consensus on which method is more accurate. However, it is generally accepted that 3D measurements are more reliable, yet most 3D reconstruction software currently in clinical use have never been directly compared to 2D. The purpose of this study is to directly compare 2D and 3D glenoid version measurements and determine the differences between the two.

METHODS

CT scans were performed preoperatively on 315 shoulders undergoing either anatomic or reverse TSA. 2D measurements of glenoid version were obtained manually using the Friedman method, whereas 3D measurements were obtained using the Equinoxe Planning Application (Exactech Inc.) 3D-reconstruction software. Negative version values indicate retroversion, whereas positive values indicate anteversion. Two observers collected the 2D measurements 2 separate times, and intra- and interobserver measurements were calculated. Groups were compared for variability using intraclass correlation coefficients (ICCs), and for differences in sample means using Student t tests. Additionally, samples were stratified by version value in order to better understand the potential sources of error between measurement techniques.

RESULTS

For the 2D measurements, intraobserver variability indicated excellent reproducibility for both observer 1 (ICC = 0.928, 95% confidence interval [CI] 0.911-0.942) and observer 2 (ICC = 0.964, 95% CI 0.955-0.971). Interobserver variability measurements also indicated excellent reproducibility (ICC = 0.915, 95% CI 0.778-0.956). The overall 2D version measurement average (-4.9° ± 10.3°) was significantly less retroverted than the 3D measurement average (-8.4° ± 9.1°) (P < .001), with 3D measurements yielding a more retroverted value 73% of the time. When stratified on the basis of version value with outliers excluded, there was no significant difference in the distribution of high-error samples within the data.

DISCUSSION

There was excellent reproducibility between the 2 observers in terms of both intra- and interobserver variability. The 3D measurement techniques were significantly more likely to return a more retroverted measurement, and high-error samples were evenly distributed throughout the data, indicating that there were no discernable trends in the degree of error observed. Shoulder surgeons should be aware that different glenoid version measurement strategies can yield different version measurements, as these can affect preoperative planning and surgeon decision making.