Current peri-operative imaging concepts surrounding shoulder arthroplasty

Superior humeral head osteophytes are associated with rotator cuff insufficiency in glenohumeral osteoarthritis: a retrospective analysis

PURPOSE

The primary goal of this study was to investigate whether superior humeral head osteophyte (SHO) size is associated with rotator cuff insufficiency, including rotator cuff tear (RCT), supraspinatus tendon thickness, and fatty infiltration of the rotator cuff muscles.

METHODS

Patients  ≥ 18 years who were diagnosed with glenohumeral osteoarthritis were retrospectively reviewed. SHO size was determined by radiograph. MRI measured SHO and RCT presence, type, and size; supraspinatus tendon thickness; and fatty infiltration of rotator cuff musculature.

RESULTS

A total of 461 patients were included. Mean SHO size was 1.93 mm on radiographs and 2.13 mm on MRI. Risk ratio for a RCT was 1.14. For each 1-mm increase in SHO size on radiograph, supraspinatus tendon thickness decreased by 0.20 mm. SHO presence was associated with moderate-to-severe fatty infiltration of the supraspinatus with a risk ratio of 3.16.

CONCLUSION

SHOs were not associated with RCT but were associated with higher risk of supraspinatus FI and decreased tendon thickness, which could indicate rotator cuff insufficiency.

LEVEL OF EVIDENCE

IV.

Shoulder joint arthroplasty in young patients: Analysis of 8742 patients from the Australian Orthopaedic Association National Joint Replacement Registry

Background

Shoulder replacement is a reliable treatment for the relief of pain and improvement of function in patients with glenohumeral arthritis, rotator cuff arthropathy, osteonecrosis and fracture. Limited data is available comparing revision rates for the different types of shoulder replacement when used in younger patients. This study aims to compare the survivorship of hemi resurfacing, stemmed hemiarthroplasty, total shoulder arthroplasty and reverse total shoulder arthroplasty in younger patients using data from a large national arthroplasty registry.

Methods

Data from the Australian Orthopaedic Association National Joint Replacement Registry was obtained for the period 16 April 2004-31 December 2018. The study population included all shoulder arthroplasty patients aged <65 years. These were stratified into two groups: <55 years and 55-64 years. A total of 8742 primary shoulder arthroplasty procedures were analysed (1936 procedures in the <55 years and 6806 in the 55-64 years age group).

Results

In the <55 years age group, there was no difference in revision rate for total shoulder arthroplasty versus reverse total shoulder arthroplasty at any time point. Reverse total shoulder arthroplasty had a lower revision rate after six months when compared to hemi resurfacing (HRA) (p = 0.031). Also, reverse total shoulder arthroplasty had a higher early rate of revision in the first 12 months compared to hemiarthroplasty (p = 0.018). However, from 2 years reverse total shoulder arthroplasty had a lower revision rate overall (p = 0.029).In the 55-64 years patient age group, reverse total shoulder arthroplasty had a lower earlier revision rate. This was statistically significant compared to hemi resurfacing (HRA) (p = 0.028), hemiarthroplasty (p = 0.049) and total shoulder arthroplasty (p < 0.001).

Conclusion

This study demonstrated that for patients aged <55 years there was no significant difference in the rate of revision when total shoulder arthroplasty and reverse total shoulder arthroplasty were compared. reverse total shoulder arthroplasty had a lower rate of revision when compared to hemi resurfacing and hemiarthroplasty after 2 years. reverse total shoulder arthroplasty had the lowest comparative revision rate in patients aged 55-64 years overall.

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.

Three-dimensional muscle loss assessment: a novel computed tomography-based quantitative method to evaluate rotator cuff muscle fatty infiltration

BACKGROUND

Rotator cuff fatty infiltration (FI) is one of the most important parameters to predict the outcome of certain shoulder conditions. The primary objective of this study was to define a new computed tomography (CT)-based quantitative 3-dimensional (3D) measure of muscle loss (3DML) based on the rationale of the 2-dimensional (2D) qualitative Goutallier score. The secondary objective of this study was to compare this new measurement method to traditional 2D qualitative assessment of FI according to Goutallier et al and to a 3D quantitative measurement of fatty infiltration (3DFI).

MATERIALS AND METHODS

102 CT scans from healthy shoulders (46) and shoulders with cuff tear arthropathy (21), irreparable rotator cuff tears (18), and primary osteoarthritis (17) were analyzed by 3 experienced shoulder surgeons for subjective grading of fatty infiltration according to Goutallier, and their rotator cuff muscles were manually segmented. Quantitative 3D measurements of fatty infiltration (3DFI) were completed. The volume of muscle fibers without intramuscular fat was then calculated for each rotator cuff muscle and normalized to the patient's scapular volume to account for the effect of body size (NV_fibers). 3D muscle mass (3DMM) was calculated by dividing the NV_fibers value of a given muscle by the mean expected volume in healthy shoulders. 3D muscle loss (3DML) was defined as 1 - (3DMM). The correlation between Goutallier grading, 3DFI, and 3DML was compared using a Spearman rank correlation.

RESULTS

Interobserver reliability for the traditional 2D Goutallier grading was moderate for the infraspinatus (ISP, 0.42) and fair for the supraspinatus (SSP, 0.38), subscapularis (SSC, 0.27) and teres minor (TM, 0.27). 2D Goutallier grading was found to be significantly and highly correlated with 3DFI (SSP, 0.79; ISP, 0.83; SSC, 0.69; TM, 0.45) and 3DML (SSP, 0.87; ISP, 0.85; SSC, 0.69; TM, 0.46) for all 4 rotator cuff muscles (P < .0001). This correlation was significantly higher for 3DML than for the 3DFI for SSP only (P = .01). The mean values of 3DFI and 3DML were 0.9% and 5.3% for Goutallier 0, 2.9% and 25.6% for Goutallier 1, 11.4% and 49.5% for Goutallier 2, 20.7% and 59.7% for Goutallier 3, and 29.3% and 70.2% for Goutallier 4, respectively.

CONCLUSION

The Goutallier score has been helping surgeons by using 2D CT scan slices. However, this grading is associated with suboptimal interobserver agreement. The new measures we propose provide a more consistent assessment that correlates well with Goutallier's principles. As 3DML measurements incorporate atrophy and fatty infiltration, they could become a very reliable index for assessing shoulder muscle function. Future algorithms capable of automatically calculating the 3DML of the cuff could help in the decision process for cuff repair and the choice of anatomic or reverse shoulder arthroplasty.

CT-based volumetric assessment of rotator cuff muscle in shoulder arthroplasty preoperative planning

Aims

The aim of this study was to describe a quantitative 3D CT method to measure rotator cuff muscle volume, atrophy, and balance in healthy controls and in three pathological shoulder cohorts.

Methods

In all, 102 CT scans were included in the analysis: 46 healthy, 21 cuff tear arthropathy (CTA), 18 irreparable rotator cuff tear (IRCT), and 17 primary osteoarthritis (OA). The four rotator cuff muscles were manually segmented and their volume, including intramuscular fat, was calculated. The normalized volume (NV) of each muscle was calculated by dividing muscle volume to the patient’s scapular bone volume. Muscle volume and percentage of muscle atrophy were compared between muscles and between cohorts.

Results

Rotator cuff muscle volume was significantly decreased in patients with OA, CTA, and IRCT compared to healthy patients (p < 0.0001). Atrophy was comparable for all muscles between CTA, IRCT, and OA patients, except for the supraspinatus, which was significantly more atrophied in CTA and IRCT (p = 0.002). In healthy shoulders, the anterior cuff represented 45% of the entire cuff, while the posterior cuff represented 40%. A similar partition between anterior and posterior cuff was also found in both CTA and IRCT patients. However, in OA patients, the relative volume of the anterior (42%) and posterior cuff (45%) were similar.

Conclusion

This study shows that rotator cuff muscle volume is significantly decreased in patients with OA, CTA, or IRCT compared to healthy patients, but that only minimal differences can be observed between the different pathological groups. This suggests that the influence of rotator cuff muscle volume and atrophy (including intramuscular fat) as an independent factor of outcome may be overestimated.

Cite this article: Bone Jt Open 2021;2(7):552–561.

Automated detection and classification of shoulder arthroplasty models using deep learning

OBJECTIVE

To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models.

MATERIALS AND METHODS

We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions.

RESULTS

The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features.

CONCLUSION

DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Objectives

This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration.

Methods

One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters.

Results

Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good–very good estimates of muscle atrophy (R² = 0.87), fatty infiltration (R² = 0.91), and overall muscle degeneration (R² = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R² = 0.61) than human raters (R² = 0.87).

Conclusions

Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters.

Key Points

• Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections.

• Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans.

• The accuracy of our automatic quantitative technique is comparable with that of human raters.

Periprosthetic Fractures in Reverse Total Shoulder Arthroplasty: Current Concepts and Advances in Management

PURPOSE OF REVIEW

Expanded indications for reverse total shoulder arthroplasty (RSA) have raised awareness of associated complications, including periprosthetic fractures. The purpose of this article was to provide a comprehensive update on how, when, and why RSA-related periprosthetic fractures occur, as well as to describe the current treatment strategies.

RECENT FINDINGS

Periprosthetic acromial and scapular spine fractures occur in up to 4.3% of cases and periprosthetic humeral fractures occur in approximately 3.5% of RSA procedures. Fractures of the coracoid process and clavicle have also been reported. Current literature has identified several risk factors for intraoperative or postoperative fracture, including underlying osteoporosis, revision arthroplasty, use of a superiorly placed screw during metaglene fixation, and disruption of the scapular ring by transection of the coracoacromial ligament. Periprosthetic fracture associated with RSA is a clinically significant event that warrants prolonged postoperative vigilance, timely diagnosis, and shared patient decision-making regarding treatment. Further research is needed to identify optimal treatment strategies and characterize long-term clinical outcomes following RSA-related periprosthetic fracture.