The Use of Glenoid Structural Allografts for Glenoid Bone Defects in Reverse Shoulder Arthroplasty.

Background: The use of reverse shoulder arthroplasty as a primary and revision implant is increasing. Advances in implant design and preoperative surgical planning allow the management of complex glenoid defects. As the demand for treating severe bone loss increases, custom allograft composites are needed to match the premorbid anatomy. Baseplate composite structural allografts are used in patients with eccentric and centric defects to restore the glenoid joint line. Preserving bone stock is important in younger patients where a revision surgery is expected. The aim of this article is to present the assessment, planning, and indications of femoral head allografting for bony defects of the glenoid. Methods: The preoperative surgical planning and the surgical technique to execute the plan with a baseplate composite graft are detailed. The preliminary clinical and radiological results of 29 shoulders which have undergone this graft planning and surgical technique are discussed. Clinical outcomes included visual analogue score of pain (VAS), American Shoulder and Elbow Surgeons score (ASES), Constant-Murley score (CS), satisfaction before and after operation, and active range of motion. Radiological outcomes included graft healing and presence of osteolysis or loosening. Results: The use of composite grafts in this series has shown excellent clinical outcomes, with an overall graft complication rate in complex bone loss cases of 8%. Conclusion: Femoral head structural allografting is a valid and viable surgical option for glenoid bone defects in reverse shoulder arthroplasty.

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Restoration of glenoid joint line: a three-dimensional analysis of scapular landmarks.

Background: Restoration of the glenoid joint line in shoulder arthroplasty is important for implant positioning and function. Medialization of the glenohumeral joint line due to glenoid bone loss is commonly encountered in primary and revision of shoulder arthroplasty albeit the direction and location of bone loss varies with different pathology. Three-Dimensional (3D) planning software has assisted in preoperative planning of complex glenoid deformities. However, limited literature is available defining a reliable 3D method to evaluate the glenoid joint line preoperatively. Aims: The purpose of this study is to identify a set of reliable scapular landmarks to be used as reference points to measure the premorbid glenoid joint line using 3D segmented models of healthy scapulae. Methods: Bilateral computed tomography scans from 79 patients eligible for primary stabilization procedures were retrospectively selected from our institutional surgical database (mean age 35 ± 10 years, 58 males and 21 females). 3D models of the contralateral healthy scapulae were created via computed tomography scan segmentation using Mimics 24.0 software (Materialise, Leuven, Belgium). Anatomical landmarks were identified using 3-Matic 16.0 software (Materialise, Leuven, Belgium). The distance between identified landmarks and a sagittal plane created on the deepest point of the glenoid was recorded for each scapula and reliability of each landmark was assessed. Inter- and intra-observer reliabilities were also evaluated using intraclass correlation coefficients (ICCs). Results: Four landmarks showed statistically significant results: the scapular notch (SN), the centroid of the coracoid (CC), a point on the most medial border of the scapula in line with the scapular spine (TS), and the most lateral point of the acromion (AL). The mean (± standard deviation) joint line measured from the SN, CC, TS and AL were 28.36 ± 2.97 mm, 11.66 ± 2.07 mm, 107.52 ± 8.1 mm, and 29.72 ± 4.46 mm, respectively. Inter-observer reliability analysis for SN, TS, and AL showed excellent agreement with ICC values of 0.966, 0.997, and 0.944, respectively, and moderate agreement for CC with ICC of 0.728. Conclusion: The results from this study assist in estimating joint line medialization preoperatively and in planning its subsequent restoration. A set of reliable landmarks can be used as references to estimate the premorbid glenoid joint line preoperatively.

The effects of anatomical errors on shoulder kinematics computed using multi-body models.

Joint motion calculated using multi-body models and inverse kinematics presents many advantages over direct marker-based calculations. However, the sensitivity of the computed kinematics is known to be partly caused by the model and could also be influenced by the participants' anthropometry and sex. This study aimed to compare kinematics computed from an anatomical shoulder model based on medical images against a scaled-generic model and quantify the effects of anatomical errors and participants' anthropometry on the calculated joint angles. Twelve participants have had planar shoulder movements experimentally captured in a motion lab, and their shoulder anatomy imaged using an MRI scanner. A shoulder multi-body dynamics model was developed for each participant, using both an image-based approach and a scaled-generic approach. Inverse kinematics have been performed using the two different modelling procedures and the three different experimental motions. Results have been compared using Bland-Altman analysis of agreement and further analysed using multi-linear regressions. Kinematics computed via an anatomical and a scaled-generic shoulder models differed in average from 3.2 to 5.4 degrees depending on the task. The MRI-based model presented smaller limits of agreement to direct kinematics than the scaled-generic model. Finally, the regression model predictors, including anatomical errors, sex, and BMI of the participant, explained from 41 to 80% of the kinematic variability between model types with respect to the task. This study highlighted the consequences of modelling precision, quantified the effects of anatomical errors on the shoulder kinematics, and showed that participants' anthropometry and sex could indirectly affect kinematic outcomes.

Single-Stage Revision Reverse Shoulder Arthroplasty: Preoperative Planning, Surgical Technique, and Mixed Reality Execution.

Revision shoulder arthroplasty is increasing with the number of primary shoulder replacements rising globally. Complex primary and revisions of shoulder arthroplasties pose specific challenges for the surgeon, which must be addressed preoperatively and intraoperatively. This article aimed to present strategies for the management of revision of shoulder arthroplasties through a single-stage approach. Preoperatively, patient factors, such as age, comorbidities, and bone quality, should be considered. The use of planning software can aid in accurately evaluating implants in situ and predict bony anatomy that will remain after explantation during the revision surgery. The planning from such software can then be executed with the help of mixed reality technology to allow accurate implant placement. Single-stage revision is performed in two steps (debridement as first step, implantation and reconstruction as the second step), guided by the following principles: adequate debridement while preserving key soft tissue attachments (i.e., rotator cuff, pectoralis major, latissimus dorsi, deltoid), restoration of glenoid joint line using bone grafting, restoration of humeral length, reconstruction and/or reattachment of soft tissues, and strict compliance with the postoperative antibiotic regimen. Preliminary results of single-stage revision shoulder arthroplasty show improvement in patient outcomes (mean 1 year), successful treatment of infection for those diagnosed with periprosthetic joint infection, and improved cost-benefit parameters for the healthcare system.