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Objectives: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis., Materials and Methods: First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae., Results: The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R 2  = 0.95), glenoid inclination (R 2  = 0.93), critical shoulder angle (R 2  = 0.95), glenopolar angle (R 2  = 0.90), glenoid height (R 2  = 0.88) and width (R 2  = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001)., Conclusions: This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Static posterior subluxation of the humeral head (SPSH) results in glenohumeral osteoarthritis. Treatment strategies for SPSH with or without resulting osteoarthritis remain challenging. There is growing interest in evaluating the rotator cuff muscle volume, fatty infiltration, or forces in osteoarthritic shoulders with SPSH, mainly due to a possible transverse force imbalance. In nonpathological shoulders, the transverse angle of the rotator cuff muscle's resultant force may be associated with scapulohumeral alignment and glenoid vault morphology, despite an assumed transverse force balance. The purpose of this study was to assess the transverse rotator cuff muscle's resultant force angle (TRFA) and its relationship with the scapulohumeral subluxation index (SHSI) and selected glenoid vault parameters using computer modeling., Methods: Computed tomography scans of 55 trauma patients (age 31 ± 13 years, 36 males) with nonpathological shoulders were analyzed and all measurements performed in 3-dimension. We placed landmarks manually to determine the humeral head center and the rotator cuff tendon footprints. The contours of the rotator cuff muscle cross-sectional areas were automatically predicted in a plane perpendicular to the scapula. Each rotator cuff muscle was divided into virtual vector fibers with homogeneous density. The resultant force vector direction for each muscle, corresponding to the rotator cuff action line, was calculated by vectorially summing the normalized fiber vectors for each muscle, weighted by the muscle trophic ratio. The resultant force vector was projected on the axial plane, and its angle with the mediolateral scapular axis was used to determine TRFA. The SHSI according to Walch, glenoid version angle (GVA), glenoid anteroposterior offset angle (GOA), glenoid depth, glenoid width, and glenoid radius were also evaluated., Results: The mean values for TRFA, SHSI, GVA, GOA, glenoid depth, glenoid width, and glenoid radius were 7.4 ± 4.5°, 54.3 ± 4.8%, -4.1 ± 4.4°, 5.1 ± 10.8°, 3.3 ± 0.6 mm, 20 ± 2 mm, and 33.6 ± 4.6 mm, respectively. The TRFA correlated strongly with SHSI (R = 0.731, P < .001) and GVA (R = 0.716, P < .001) and moderately with GOA (R = 0.663, P < .001). The SHSI was strongly negatively correlated with GVA (R = -0.813, P < .001) and moderately with GOA (R = -0.552, P < .001). The GVA correlated strongly with GOA (R = 0.768, P < .001). In contrast, TRFA, SHSI, GVA, and GOA did not correlate with glenoid depth, width, or radius., Conclusion: Despite an assumed balance in the transverse volume of the rotator cuff muscles in nonpathological shoulders, variations exist regarding the transverse resultant force depending on the SHSI, GVA, and GOA. In healthy/nonosteoarthritic shoulders, an increased glenoid retroversion is associated with a decreased anterior glenoid offset., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Although calcium pyrophosphate deposition (CPPD) is common, there are no published outcome domains or validated measurement instruments for CPPD studies. In this paper, we describe the framework for development of the Outcome Measures in Rheumatology (OMERACT) CPPD Core Domain Sets., Methods: The OMERACT CPPD working group performed a scoping literature review and qualitative interview study. Generated outcomes were presented at the 2020 OMERACT CPPD virtual Special Interest Group (SIG) meeting with discussion focused on whether different core domain sets should be developed for different calcium pyrophosphate deposition (CPPD) clinical presentations and how the future CPPD Core Domain Set may overlap with already established osteoarthritis (OA) domains. These discussions informed development of a future work plan for development of the OMERACT CPPD Core Domain Sets., Findings: Domains identified from a scoping review of 112 studies and a qualitative interview study of 36 people (28 patients with CPPD, 7 health care professionals, one stakeholder) were mapped to core areas of OMERACT Filter 2.1. The majority of SIG participants agreed there was need to develop separate core domain sets for "short term" and "long term" studies of CPPD. Although CPPD + OA is common and core domain sets for OA have been established, participants agreed that existing OA core domain sets should not influence the development of OMERACT core domain sets for CPPD. Prioritization exercises (using Delphi methodology) will consider 40 potential domains for short term studies of CPPD and 47 potential domains for long term studies of CPPD., Conclusion: Separate OMERACT CPPD Core Domain Sets will be developed for "short term" studies for an individual flare of acute CPP crystal arthritis and for "long term" studies that may include participants with any clinical presentation of CPPD (acute CPP crystal arthritis, chronic CPP crystal inflammatory arthritis, and/or CPPD + OA)., Competing Interests: Declaration of Competing Interest KC reports a research fellowship grant from Arthritis Australia. RC is a member of the Technical Advisory Group of OMERACT. JAS has received consultant fees from Crealta/Horizon, Medisys, Fidia, PK Med, Two labs Inc, Adept Field Solutions, Clinical Care options, Clearview healthcare partners, Putnam associates, Focus forward, Navigant consulting, Spherix, MedIQ, Jupiter Life Science, UBM LLC, Trio Health, Medscape, WebMD, and Practice Point communications; and the National Institutes of Health and the American College of Rheumatology. JAS owns stock options in TPT Global Tech, Vaxart pharmaceuticals and Charlotte's Web Holdings, Inc. JAS previously owned stock options in Amarin, Viking and Moderna pharmaceuticals. JAS is on the speaker's bureau of Simply Speaking. JAS is a member of the executive of OMERACT. DG has a private equity holding in Charlotte's Web Holdings, Inc. ND reports grants and personal fees from AstraZeneca, grants from Amgen, personal fees from Dyve, personal fees from Hengrui, personal fees from Selecta, personal fees from Horizon, personal fees from Abbvie, personal fees from Pfizer, personal fees from Janssen, personal fees from Arthrosi, outside the submitted work. AA and SKT have received consultant fees from NGM Biopharmaceuticals. FB has received personal consultant fees from Horizon Therapeutics. OMERACT, an organization that develops outcome measures in rheumatology, receives arms-length funding from 8 companies. The other authors have no disclosures., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: This study proposes a method for inferring the premorbid glenoid shape and orientation of scapulae affected by glenohumeral osteoarthritis (OA) to inform restorative surgery., Methods: A statistical shape model (SSM) built from 64 healthy scapulae was used to reconstruct the premorbid glenoid shape based on anatomic features that are considered unaffected by OA. First, the method was validated on healthy scapulae by quantifying the accuracy of the predicted shape in terms of surface distance, glenoid version, and inclination. The SSM-based reconstruction was then applied to 30 OA scapulae. Glenoid version and inclination were measured fully automatically and compared between the original OA glenoids, SSM-based glenoid reconstructions, and healthy scapulae., Results: Validation on healthy scapulae showed a root-mean-square surface distance between original and predicted glenoids of 1.0 ± 0.2 mm. The prediction error was 2.3° ± 1.8° for glenoid version and 2.1° ± 2.0° for inclination. When applied to an OA dataset, SSM-based reconstruction restored average glenoid version and inclination to values similar to the healthy situation. No differences were observed between average orientation values measured on SSM-based reconstructed and healthy scapulae (P ≥ .10). However, the average orientation of the reconstructed premorbid glenoid differed from the average orientation of OA glenoids for Walch classes A1 (version) and B2 (version, inclination, and medialization)., Conclusion: The proposed SSM can predict the premorbid glenoid cavity of healthy scapulae with millimeter accuracy. This technique has the potential to reconstruct the premorbid glenoid cavity shape, as it was prior to OA, and thus to guide the positioning of glenoid implants in total shoulder arthroplasty., (Copyright © 2018 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Background: We evaluated the biomechanical effects and potential advantages of glenoid implants with adaptable backside curvature radii and compared them with standard implants having fixed backside curvatures in anatomic total shoulder arthroplasty (aTSA) for primary glenohumeral osteoarthritis with uniconcave glenoids., Methods: A glenoid implant with adaptable backside curvatures (Aequalis PerFORM, Tornier SAS, Montbonnot, France) was compared with its previous model having a fixed curvature radius. Virtual aTSAs were performed in 24 patients from preoperative shoulder computed tomography data sets, using both implants in each patient. For all 48 simulated aTSAs, we first measured the glenoid bone reaming depth, subchondral bone quality after reaming, and implant backside surface and then the predicted cement stress, bone-cement interfacial stress, and bone strain at 60° of arm abduction. These biomechanical quantities were tested for differences between adaptable and fixed implants and for correlations between preoperative measurements and postoperative predictions., Results: Adaptable glenoid implants induced a significant decrease in cement stress (P = .008), bone-cement interfacial stress (P = .045), and bone strain (P = .039), particularly for glenoids with curvature radii larger than 40 mm. However, these biomechanical effects were not significantly correlated with an increase in subchondral glenoid bone quality., Conclusions: Our study confirms the presumed biomechanical advantages of adaptable glenoid implants, even though the effects were not directly due to the adaptation of the backside curvature radius. Benefits were more pronounced for glenoids with large curvature radii. Our initial biomechanical findings should now be corroborated with large-scale clinical studies., (Copyright © 2018 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Background: Previous clinical studies have reported associations between glenoid inclination (GI), the acromion index (AI), and the critical shoulder angle (CSA) on the one hand and the occurrence of glenohumeral osteoarthritis and supraspinatus tendon tears on the other hand. The objective of this work was to analyze the correlations and relative importance of these different anatomic parameters., Methods: Using a musculoskeletal shoulder model developed from magnetic resonance imaging scans of 1 healthy volunteer, we varied independently GI from 0° to 15° and AI from 0.5 to 0.8. The corresponding CSA varied from 20.9° to 44.1°. We then evaluated humeral head translation and critical strain volume in the glenoid articular cartilage at 60° of abduction in the scapular plane. These values were correlated with GI, AI, and CSA., Results: Humeral head translation was positively correlated with GI (R = 0.828, P < .0001), AI (R = 0.539, P < .0001), and CSA (R = 0.964, P < .0001). Glenoid articular cartilage strain was also positively correlated with GI (R = 0.489, P = .0004) but negatively with AI (R = -0.860, P < .0001) and CSA (R = -0.285, P < .0473)., Conclusions: The biomechanical shoulder model is consistent with clinical observations. The prediction strength of CSA is confirmed for humeral head translation and thus presumably for rotator cuff tendon tears, whereas the AI seems more appropriate to evaluate the risk of glenohumeral osteoarthritis caused by excessive articular cartilage strain. As a next step, we should corroborate these theoretical findings with clinical data., (Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)