Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty

Do constrained liners (in a 145° onlay implant) provide any benefit? A matched retrospective study

BACKGROUND

The purpose of this study was to compare the outcomes of primary reverse total shoulder arthroplasty (rTSA) using constrained liners (in a 145° onlay implant, Equinoxe [Exactech]) with primary rTSA using standard liners with a minimum 1-year follow-up.

METHODS

A total of 836 primary rTSA patients were analyzed in this study. Patients treated with constrained liners (n = 209) were cross-matched 1:3 for age, gender, glenosphere diameter, and follow-up duration and compared with 627 patients who underwent primary rTSA with standard liners. Study endpoint was at 1 year. Outcomes were analyzed preoperatively and at the latest follow-up. Patient characteristics, postoperative range of motion (ROM), patient-reported outcomes (PROs), complications and revisions were recorded.

RESULTS

There was no statistically significant changes in improvement in pain (-4.9 vs. -5.1; P = .356), ROM (abduction, 45.7° vs. 47.9°, P = .522; forward elevation, 44.0° vs. 50.8°, P = .057; internal rotation score 1.0 vs. 1.1, P = .709; and external rotation, 17.9° vs. 16.7°, P = .543), or PROs (American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form [ASES] score, 44.5 vs. 43.7, P = .107; and Shoulder Arthroplasty Smart [SAS] score, 27.5 vs. 30.0, P = .052) between the constrained and standard liner cohorts at minimum 1-year follow-up. However, the constrained liner rTSA cohort had a significantly higher rate of adverse events (6.2% vs. 2.7%, P = .012), including a higher rate of scapular notching (15.6% vs. 8.8%, P = .015).

CONCLUSION

The use of constrained liners in primary rTSA demonstrated no significant difference in the change in pain, abduction, forward elevation, external and internal rotation scores, ASES scores, and SAS scores at minimum 1-year follow-up. There was no significant difference in forward elevation or abduction compared with standard liners. However, we observed that the overall rate of adverse events, including scapular notching, were significantly higher in the constrained liner cohort. Long-term clinical and radiographic follow-up is necessary to fully elucidate the durability of these results. At this time, it is unclear if constrained liners have any benefit in rTSA.

Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review

Purpose

The aim of the present review is to evaluate and report on the available literature discussing artificial intelligence (AI) applications to the diagnosis of shoulder conditions, outcome prediction of shoulder interventions, and the possible application of such algorithms directly to surgical procedures.

Methods

In February 2024, a search of PubMed, Cochrane and Scopus databases was performed. Studies had to evaluate AI model effectiveness for inclusion. Research on healthcare cost predictions, deterministic algorithms, patient satisfaction, protocol studies and upper-extremity fractures not involving the shoulder were excluded. The Joanna Briggs Institute Critical Appraisal tool and the Risk of Bias in Non-randomised Studies of Interventions tools were used to assess bias.

Results

Thirty-three studies were included in the analysis. Seven studies analysed the detection of rotator cuff tears (RCTs) in magnetic resonance imaging and found area under the curve (AUC) values ranged from 0.812 to 0.94 for the detection of RCTs. One study reported Area Under the Receiver Operating Characteristics values ranging from 0.79 to 0.97 for the prediction of clinical outcomes following reverse total shoulder arthroplasty. In terms of outcomes of rotator cuff repair, an AUC value ranging from 0.58 to 0.68 was reported for prediction of patient-reported outcome measures, and an AUC range of 0.87-0.92 was found for prediction of retear rate. Five studies evaluated the identification of shoulder implant models following TSA from radiographs, with reported accuracy ranging from 89.90% to 97.20%.

Conclusion

AI application enables forecasting of clinical outcomes, permits refined diagnostic evaluation and increases surgical accuracy. While promising, the translation of these technologies into routine clinical practice requires careful consideration.

Level of Evidence

Level IV.

Does matching glenosphere size to patient height improve outcomes following reverse total shoulder arthroplasty?

Introduction

Optimal biomechanics in reverse total shoulder arthroplasty (rTSA) are still a topic of debate. Although larger glenospheres have been linked with a theoretical improvement in the range of movement, results from clinical studies are mixed. We hypothesised that matching glenosphere diameter to patient height would result in greater improvements in post-operative range of motion (ROM) and patient-reported outcomes (PROMs).

Methods

An international database of rTSAs was analysed. After exclusions, 3318 rTSA patients were classified as short (<158 cm), average (158-173 cm) or tall(>173 cm). Outcomes were stratified for glenosphere size (small≤38 mm, large≥40 mm). Results were compared preoperatively and at 2 years.

Results

In short patients glenosphere diameter had no statistically significant impact on the degree of post-operative improvement for any ROM or PROM. Average height patients treated with small glenospheres had significantly more improvement in internal rotation (1.3 vs 1.0, p = 0.01), VAS pain (5.3 vs 4.8, p = 0.002), American Shoulder and Elbow Surgeons (47.8 vs 45.2, p = 0.03) and Shoulder Arthroplasty Smart (30.9 vs 28.2, p = 0.01) but significantly less improvement in constant score (31.7 vs 35.3, p = 0.009). Tall patients treated with small glenospheres had significantly more improvement in external rotation (21.2 vs 16.4, p = 0.01) and VAS pain scores (4.7 vs 4.3, p = 0.04).

Conclusions

While most significant differences favoured small glenospheres, the magnitude of these differences was small. Overall, patients of all heights can expect similar clinical improvements irrespective of glenosphere size.

Machine learning in shoulder arthroplasty : a systematic review of predictive analytics applications

Aims

Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis.

Methods

We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes.

Results

Following the final screening process, 25 articles satisfied the eligibility criteria for our review. Of these, 60% focused on tabular data while the remaining 40% analyzed image data. Among them, 16 studies were dedicated to developing new models and nine used transfer learning to leverage existing pretrained models. Additionally, three of these models underwent external validation to confirm their reliability and effectiveness.

Conclusion

ML algorithms used in TSA demonstrated fair to good performance, as evidenced by the reported metrics. Integrating these models into daily clinical practice could revolutionize TSA, enhancing both surgical precision and patient outcome predictions. Despite their potential, the lack of transparency and generalizability in many current models poses a significant challenge, limiting their clinical utility. Future research should prioritize addressing these limitations to truly propel the field forward and maximize the benefits of ML in enhancing patient care.

Impact of accumulating risk factors on the incidence of dislocation after primary reverse total shoulder arthroplasty using a medial glenoid-lateral humerus onlay prosthesis

BACKGROUND

The aim of this study was to facilitate preoperative identification of patients at risk for dislocation after reverse total shoulder arthroplasty (rTSA) using the Equinoxe rTSA prosthesis (medialized glenoid, lateralized onlay humerus with a 145° neck-shaft angle) and quantify the impact of accumulating risk factors on the occurrence of dislocation.

METHODS

We retrospectively analyzed 10,023 primary rTSA patients from an international multicenter database of a single platform shoulder prosthesis and quantified the dislocation rate associated with multiple combinations of previously identified risk factors. To adapt our statistical results for prospective identification of patients most at-risk for dislocation, we stratified our data set by multiple risk factor combinations and calculated the odds ratio for each cohort to quantify the impact of accumulating risk factors on dislocation.

RESULTS

Of the 10,023 primary rTSA patients, 136 (52 female, 83 male, 1 unknown) were reported to have a dislocation for a rate of 1.4%. Patients with zero risk factors were rare, where only 12.7% of patients (1268 of 10,023) had no risk factors, and only 0.5% of these (6 of 1268) had a report of dislocation. The dislocation rate increased in patient cohorts with an increasing number of risk factors. Specifically, the dislocation rate increased from 0.9% for a patient cohort with 1 risk factor to 1.0% for 2 risk factors, 1.6% for 3 risk factors, 2.7% for 4 risk factors, 5.3% for 5 risk factors, and 7.3% for 6 risk factors. Stratifying dislocation rate by multiple risk factor combinations identified numerous cohorts with either an elevated risk or a diminished risk for dislocation.

DISCUSSION

This multicenter study of 10,023 rTSA patients demonstrated that 1.4% of the patients experienced dislocation with one specific medialized glenoid-lateralized humerus onlay rTSA prosthesis. Stratifying patients by multiple combinations of risk factors demonstrated the impact of accumulating risk factors on the incidence of dislocation. rTSA patients with the greatest risk of dislocation were those of male sex, age ≤67 years at the time of surgery, patients with body mass index ≥31, patients who received cemented humeral stems, patients who received glenospheres having a diameter >40 mm, and/or patients who received expanded or laterally offset glenospheres. Patients with these risk factors who are considering rTSA using a medial glenoid-lateral humerus should be made aware of their elevated dislocation risk profile.

Recent Advances in the Design and Application of Shoulder Arthroplasty Implant Systems and Their Impact on Clinical Outcomes: A Comprehensive Review

Purpose of Review

This narrative review comprehensively aims to analyze recent advancements in shoulder arthroplasty, focusing on implant systems and their impact on patient outcomes. The purpose is to provide a nuanced understanding of the evolving landscape in shoulder arthroplasty, incorporating scientific, regulatory, and ethical dimensions.

Recent Findings

The review synthesizes recent literature on stemless implants, augmented glenoid components, inlay vs onlay configurations, convertible stems, and associated complications. Notable findings include improved patient-reported outcomes with stemless implants, variations in outcomes between inlay and onlay configurations, and the potential advantages of convertible stems. Additionally, the regulatory landscape, particularly the FDA's 510(k) pathway, is explored alongside ethical considerations, emphasizing the need for standardized international regulations.

Summary

Recent innovations in shoulder arthroplasty showcase promising advancements, with stemless implants demonstrating improved patient outcomes. The review underscores the necessity for ongoing research to address unresolved aspects and highlights the importance of a standardized regulatory framework to ensure patient safety globally. The synthesis of recent findings contributes to a comprehensive understanding of the current state of shoulder arthroplasty, guiding future research and clinical practices.

Advanced technology in shoulder arthroplasty surgery: Artificial intelligence, extended reality, and robotics

The purpose of this review is to provide an overview of the integration of technological advancements in orthopedic shoulder surgery. Recent technological advancements in orthopedic shoulder surgery include predictive analytics, computer-navigated instrumentation for operative planning, extended reality, and robotics. Separately, these advancements provide distinct methodological attempts to improve surgical experiences and outcomes. Together, these technologies can provide orthopedic surgeons with the tools and capabilities to improve patient care and communication in shoulder arthroplasty. From artificial intelligence-generated predictive analytics to extended reality and robotics, technical innovations may lead to improvements in patient education, surgical accuracy, interdisciplinary communication, and outcomes. A comprehensive narrative review was conducted to explore the technological advancements of orthopedic shoulder arthroplasty. Our findings emphasized the impact of these advancements, exemplified by early enhancements in efficacy and safety. However, certain challenges remain, such as a lack of reproducibly improved outcomes and cost considerations. While the reviewed studies indicate hope for improving shoulder arthroplasty, the true cost-effectiveness and applicability remains to be determined, indicating the need for further research.

Artificial intelligence in shoulder and elbow surgery: overview of current and future applications

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.

Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling.

Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review

BACKGROUND

Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS

A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to predict SA outcomes. With duplicates removed, the initial query yielded 327 articles, and after applying inclusion and exclusion criteria, 12 articles that had at least 1 month follow-up time were included.

RESULTS

ML can predict 30-day postoperative complications with a 90% accuracy, postoperative range of motion with a higher-than-85% accuracy, and clinical improvement in patient-reported outcome measures above minimal clinically important differences with a 93%-99% accuracy. ML can predict length of stay, operative time, discharge disposition, and hospitalization costs.

CONCLUSION

ML can accurately predict outcomes and complications following SA and healthcare utilization. Outcomes are highly dependent on the type of algorithms used, data input, and features selected for the model.

LEVEL OF EVIDENCE

III.

Evaluating the fairness and accuracy of machine learning-based predictions of clinical outcomes after anatomic and reverse total shoulder arthroplasty

BACKGROUND

Machine learning (ML)-based clinical decision support tools (CDSTs) make personalized predictions for different treatments; by comparing predictions of multiple treatments, these tools can be used to optimize decision making for a particular patient. However, CDST prediction accuracy varies for different patients and also for different treatment options. If these differences are sufficiently large and consistent for a particular subcohort of patients, then that bias may result in those patients not receiving a particular treatment. Such level of bias would deem the CDST "unfair." The purpose of this study is to evaluate the "fairness" of ML CDST-based clinical outcomes predictions after anatomic (aTSA) and reverse total shoulder arthroplasty (rTSA) for patients of different demographic attributes.

METHODS

Clinical data from 8280 shoulder arthroplasty patients with 19,249 postoperative visits was used to evaluate the prediction fairness and accuracy associated with the following patient demographic attributes: ethnicity, sex, and age at the time of surgery. Performance of clinical outcome and range of motion regression predictions were quantified by the mean absolute error (MAE) and performance of minimal clinically important difference (MCID) and substantial clinical benefit classification predictions were quantified by accuracy, sensitivity, and the F1 score. Fairness of classification predictions leveraged the "four-fifths" legal guideline from the US Equal Employment Opportunity Commission and fairness of regression predictions leveraged established MCID thresholds associated with each outcome measure.

RESULTS

For both aTSA and rTSA clinical outcome predictions, only minor differences in MAE were observed between patients of different ethnicity, sex, and age. Evaluation of prediction fairness demonstrated that 0 of 486 MCID (0%) and only 3 of 486 substantial clinical benefit (0.6%) classification predictions were outside the 20% fairness boundary and only 14 of 972 (1.4%) regression predictions were outside of the MCID fairness boundary. Hispanic and Black patients were more likely to have ML predictions out of fairness tolerance for aTSA and rTSA. Additionally, patients <60 years old were more likely to have ML predictions out of fairness tolerance for rTSA. No disparate predictions were identified for sex and no disparate regression predictions were observed for forward elevation, internal rotation score, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form score, or global shoulder function.

CONCLUSION

The ML algorithms analyzed in this study accurately predict clinical outcomes after aTSA and rTSA for patients of different ethnicity, sex, and age, where only 1.4% of regression predictions and only 0.3% of classification predictions were out of fairness tolerance using the proposed fairness evaluation method and acceptance criteria. Future work is required to externally validate these ML algorithms to ensure they are equally accurate for all legally protected patient groups.

Initial clinical experience with a predictive clinical decision support tool for anatomic and reverse total shoulder arthroplasty

PURPOSE

Clinical decision support tools (CDSTs) are software that generate patient-specific assessments that can be used to better inform healthcare provider decision making. Machine learning (ML)-based CDSTs have recently been developed for anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty to facilitate more data-driven, evidence-based decision making. Using this shoulder CDST as an example, this external validation study provides an overview of how ML-based algorithms are developed and discusses the limitations of these tools.

METHODS

An external validation for a novel CDST was conducted on 243 patients (120F/123M) who received a personalized prediction prior to surgery and had short-term clinical follow-up from 3 months to 2 years after primary aTSA (n = 43) or rTSA (n = 200). The outcome score and active range of motion predictions were compared to each patient's actual result at each timepoint, with the accuracy quantified by the mean absolute error (MAE).

RESULTS

The results of this external validation demonstrate the CDST accuracy to be similar (within 10%) or better than the MAEs from the published internal validation. A few predictive models were observed to have substantially lower MAEs than the internal validation, specifically, Constant (31.6% better), active abduction (22.5% better), global shoulder function (20.0% better), active external rotation (19.0% better), and active forward elevation (16.2% better), which is encouraging; however, the sample size was small.

CONCLUSION

A greater understanding of the limitations of ML-based CDSTs will facilitate more responsible use and build trust and confidence, potentially leading to greater adoption. As CDSTs evolve, we anticipate greater shared decision making between the patient and surgeon with the aim of achieving even better outcomes and greater levels of patient satisfaction.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty

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.

Glenoid lateralization and subscapularis repair are independent predictive factors of improved internal rotation after reverse shoulder arthroplasty

PURPOSE

Reverse shoulder arthroplasty (RSA) has shown improvement in clinical outcomes for various conditions, although some authors expressed concern about the restoration of active internal rotation (AIR). The current study assesses preoperative and intraoperative predictive factors of AIR in patients having a Grammont-style RSA with a minimum five year follow-up.

METHODS

We conducted a retrospective multicentric study, including patients operated on with a 155° Grammont-style RSA for cuff-related pathology or primary osteoarthritis with posterior subluxation or an associated cuff tear. Patients were clinically evaluated at a minimum of five year follow-up. Patients with previous surgery or those who had a tendon transfer with the RSA were excluded. Demographic parameters, BMI, preoperative notes, and operative reports were obtained from medical records. AIR was graded according to the constant score system from 0 to 10.

RESULTS

A total of 280 shoulders in 269 patients (mean age at surgery, 74.9 ± 5.9 years) met the inclusion criteria and were analyzed. The average follow-up was 8.1 years (range, 5-16 years). Overall, AIR increased from 4.2 (SD 2.5, range 0 to 10) preoperatively to 5.9 (SD 2.6, range 0 to 10) at final follow-up. At the last follow-up, AIR increased in 56% of cases, was unchanged in 26% and decreased in 18%. In 188 shoulders (67%), internal rotation was functional and allowed patients to reach the level of L3 or higher. Multivariable linear regression found the following preoperative clinical factors predictive of worse AIR after RSA: male gender (ß = -1.25 [-2.10; -0.40]; p = 0.0042) and higher values of BMI (ß = -0.085 [-0.17; -0.0065]; p = 0.048). Two surgical factors were associated with better AIR after RSA: glenoid lateralization with BIO-RSA technique (ß = 0.80 [0.043; 1.56]; p = 0.039) and subscapularis repair (ß = 1.16 [0.29; 2.02]; p = 0.0092).

CONCLUSIONS

With a mean of eight year follow-up (5 to 16 years), internal rotation was functional (≥ L3 level) in 67% of operated shoulders after Grammont-style RSA; however, two patients out of ten had decreased AIR after surgery. Male patients and those with higher BMIs had worse AIR, with glenoid lateralization (using the BIO-RSA technique) and subscapularis repair, as they are predictive of increased AIR after RSA.

LEVEL OF EVIDENCE

Case series, Level IV.

Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next

Background

Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications.

Methods

PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery.

Results

Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology.

Conclusion

AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

Technological innovations in shoulder replacement: current concepts and the future of robotics in total shoulder arthroplasty

BACKGROUND

Total shoulder arthroplasty (TSA) has been rapidly evolving over the last several decades, with innovative technological strategies being investigated and developed in order to achieve optimal component precision and joint alignment and stability, preserve implant longevity, and improve patient outcomes. Future advancements such as robotic-assisted surgeries, augmented reality, artificial intelligence, patient-specific instrumentation (PSI) and other peri- and preoperative planning tools will continue to revolutionize TSA. Robotic-assisted arthroplasty is a novel and increasingly popular alternative to the conventional arthroplasty procedure in the hip and knee but has not yet been investigated in the shoulder. Therefore, the purpose of this study was to conduct a narrative review of the literature on the evolution and projected trends of technological advances and robotic assistance in total shoulder arthroplasty.

METHODS

A narrative synthesis method was employed for this review, rather than a meta-analysis or systematic review of the literature. This decision was based on 2 primary factors: (1) the lack of eligible, peer-reviewed studies with high-quality level of evidence available for review on robotic-assisted shoulder arthroplasty, and (2) a narrative review allows for a broader scope of content analysis, including a comprehensive review of all technological advances-including robotics-within the field of TSA. A general literature search was performed using PubMed, Embase, and Cochrane Library databases. These databases were queried by 2 independent reviewers from database inception through November 11, 2022, for all articles investigating the role of robotics and technology assistance in total shoulder arthroplasty. Inclusion criteria included studies describing "shoulder arthroplasty" and "robotics."

RESULTS

After exclusion criteria were applied, 4 studies on robotic-assisted TSA were described in the review. Given the novelty of this technology and limited data on robotics in TSA, these studies consisted of a literature review, nonvalidated experimental biomechanical studies in sawbones models, and preclinical proof-of-concept cadaveric studies using prototype robotic technology primarily in conjunction with PSI. The remaining studies described the technological advancements in TSA, including PSI, computer-assisted navigation, artificial intelligence, machine learning, and virtual, augmented, and mixed reality. Although not yet commercially available, robotic-assisted TSA confers the theoretical advantages of precise humeral head cuts for restoration of proximal humerus anatomy, more accurate glenoid preparation, and improved soft-tissue assessment in limited early studies.

CONCLUSION

The evidence for the use of robotics in total hip arthroplasty and total knee arthroplasty demonstrates improved component accuracy, more precise radiographic measurements, and improved early/mid-term patient-reported and functional outcomes. Although no such data currently exist for shoulder arthroplasty given that the technology has not yet been commercialized, the lessons learned from robotic hip and knee surgery in conjunction with its rapid adoption suggests robotic-assisted TSA is on the horizon of innovation. By achieving a better understanding of the past, present, and future innovations in TSA through this narrative review, orthopedic surgeons can be better prepared for future applications.

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Comparison of long-term clinical and radiological outcomes for cemented keel, cemented peg, and hybrid cage glenoids with anatomical total shoulder arthroplasty using the same humeral component

Aims

The aim of this study was to longitudinally compare the clinical and radiological outcomes of anatomical total shoulder arthroplasty (aTSA) up to long-term follow-up, when using cemented keel, cemented peg, and hybrid cage peg glenoid components and the same humeral system.

Methods

We retrospectively analyzed a multicentre, international clinical database of a single platform shoulder system to compare the short-, mid-, and long-term clinical outcomes associated with three designs of aTSA glenoid components: 294 cemented keel, 527 cemented peg, and 981 hybrid cage glenoids. Outcomes were evaluated at 4,746 postoperative timepoints for 1,802 primary aTSA, with a mean follow-up of 65 months (24 to 217).

Results

Relative to their preoperative condition, each glenoid cohort had significant improvements in clinical outcomes from two years to ten years after surgery. Patients with cage glenoids had significantly better clinical outcomes, with higher patient-reported outcome scores and significantly increased active range of motion, compared with those with keel and peg glenoids. Those with cage glenoids also had significantly fewer complications (keel: 13.3%, peg: 13.1%, cage: 7.4%), revisions (keel: 7.1%, peg 9.7%, cage 3.5%), and aseptic glenoid loosening and failure (keel: 4.7%, peg: 5.8%, cage: 2.5%). Regarding radiological outcomes, 70 patients (11.2%) with cage glenoids had glenoid radiolucent lines (RLLs). The cage glenoid RLL rate was 3.3-times (p < 0.001) less than those with keel glenoids (37.3%) and 4.6-times (p < 0.001) less than those with peg glenoids (51.2%).

Conclusion

These findings show that good long-term clinical and radiological outcomes can be achieved with each of the three aTSA designs of glenoid component analyzed in this study. However, there were some differences in clinical and radiological outcomes: generally, cage glenoids performed best, followed by cemented keel glenoids, and finally cemented peg glenoids.

Accuracy of Blueprint software in predicting range of motion 1 year after reverse total shoulder arthroplasty

HYPOTHESIS AND BACKGROUND

Blueprint 3-dimensional computed tomography software has a functionality that predicts impingement-free range of motion (ROM) with determination of the limits of ROM at which bone and/or prosthetic impingement occurs. To our knowledge, only 1 previously published study has assessed the ability of Blueprint software to predict actual postoperative ROM after reverse total shoulder arthroplasty (RTSA). The hypotheses of this study were that (1) mean Blueprint-predicted impingement-free ROM would be statistically similar to the mean actual ROM 1 year after RTSA and (2) there would be a correlation between Blueprint-predicted impingement-free ROM and the actual ROM 1 year after RTSA.

MATERIALS AND METHODS

A retrospective review of patients who underwent Blueprint planning prior to undergoing RTSA from March 2017 through May 2021 was performed. At 1-year follow-up, flexion, external rotation at the side, abduction, external rotation in the abducted position, internal rotation in the abducted position, and internal rotation behind the back were measured. The preoperatively predicted flexion, extension, abduction, external rotation, and internal rotation were recorded using Blueprint software. The group 1 analysis examined the predicted vs. actual ROM of all 127 patients regardless of whether intraoperative component modifications were made. The group 2 analysis examined the predicted vs. actual ROM of only the patients who did not undergo intraoperative changes that would affect the preoperative ROM prediction (n = 97). The group 3 analysis examined the predicted vs. actual ROM of group 2 combined with the 30 patients who underwent post hoc Blueprint planning modifications to account for the changes made intraoperatively (combined sample size of 127).

RESULTS

Of the 141 patients, 127 (90%) were available for 1-year follow-up. When the mean values of all 3 groups were examined, the actual ROM and predicted ROM were statistically significantly different (P < .0001) for flexion, external rotation, abduction, abduction-external rotation, and abduction-internal rotation. In group 1, a very weak or poor correlation was found between predicted internal rotation and actual abducted internal rotation (r = 0.19, P = .04). For all other ROM metrics in groups 1, 2, and 3, there were no correlations between predicted and actual ROM (P ≥ .07).

CONCLUSIONS

In its current state, preoperative Blueprint 3-dimensional computed tomography planning software is unable to accurately predict ROM 1 year after RTSA.

Reverse Shoulder Arthroplasty Biomechanics

The reverse total shoulder arthroplasty (rTSA) prosthesis has been demonstrated to be a viable treatment option for a variety of end-stage degenerative conditions of the shoulder. The clinical success of this prosthesis is at least partially due to its unique biomechanical advantages. As taught by Paul Grammont, the medialized center of rotation fixed-fulcrum prosthesis increases the deltoid abductor moment arm lengths and improves deltoid efficiency relative to the native shoulder. All modern reverse shoulder prostheses utilize this medialized center of rotation (CoR) design concept; however, some differences in outcomes and complications have been observed between rTSA prostheses. Such differences in outcomes can at least partially be explained by the impact of glenoid and humeral prosthesis design parameters, surgical technique, implant positioning, patient-specific bone morphology, and usage in humeral and glenoid bone loss situations on reverse shoulder biomechanics. Ultimately, a better understanding of the reverse shoulder biomechanical principles will guide future innovations and further improve clinical outcomes.