Advanced Templating for Total Shoulder Arthroplasty

Total Ankle Arthroplasty Templating: Preoperative Computer Templating Correlates Highly with Intraoperative Component Selection

Proper alignment and sizing are critical to the performance of a successful total ankle arthroplasty. While it is common practice in preoperative planning prior to total knee and total hip arthroplasty, preoperative computer templating has not been well established in the setting of total ankle arthroplasty. A retrospective review of all total ankle arthroplasties performed during a 10-year period by a single fellowship-trained orthopaedic surgeon was conducted. Computer templating was utilized for all preoperative Anterior to Posterior (AP) and lateral standing radiographs, and templated component sizes were compared to the operative reports and postoperative radiographs to determine the precision of the available templates. Statistical analysis was performed with Interclass Correlation Coefficients (ICC) and descriptive statistical tests. Seventy patients with a mean age of 64.8 years (range, 48-87) and mean BMI of 30.34 (range, 19.1-55.6) were included. The ICC demonstrated that both the AP (ICC 0.80 - 95% CI 0.679-0.876) and lateral (ICC 0.786 - 95% CI 0.655-0.867) radiographs provided accurate tibial total ankle arthroplasty component templating. Similarly, the AP (ICC 0.842 - 95% CI 0.745-0.902) and lateral (ICC 0.809 - 95% CI 0.692-0.881) radiographs provided accurate talar templating. No differences were observed when comparing AP to lateral radiographs in percentage of correct component templating: tibial AP 61.4% vs lateral 58.6%, p = .119 and talar component AP 57.1% vs lateral 45.7%, p = .176. These study findings demonstrate that preoperative templating for total ankle arthroplasties is accurate in determining appropriate implant sizing. Accurate templating is an absolute necessity for future templating studies.

Mixed-reality improves execution of templated glenoid component positioning in shoulder arthroplasty: a CT imaging analysis

INTRODUCTION

Glenoid placement is critical for successful outcomes in total shoulder arthroplasty (TSA). Preoperative templating with three-dimensional imaging has improved implant positioning, but deviations from the planned inclination and version still occur. Mixed-Reality (MR) is a novel technology that allows surgeons intra-operative access to three-dimensional imaging and templates, capable of overlaying the surgical field to help guide component positioning. The purpose of this study was to compare the execution of preoperative templates using MR vs.standard instruments (SIs).

METHODS

Retrospective review of 97 total shoulder arthroplasties (18 anatomic, 79 reverse) from a single high-volume shoulder surgeon between January 2021 and February 2023, including only primary diagnoses of osteoarthritis, rotator cuff arthropathy, or a massive irreparable rotator cuff tear. To be included, patients needed a templated preoperative plan and then a postoperative computed tomography scan. Allocation to MR vs. SI was based on availability of the MR headset, industry technical personnel, and the templated preoperative plan loaded into the software, but preoperative or intraoperative patient factors did not contribute to the allocation decision. Postoperative inclination and version were measured by two independent, blinded physicians and compared to the preoperative template. From these measurements, we calculated the mean difference, standard deviation (SD), and variance to compare MR and SI.

RESULTS

Comparing 25 MR to 72 SI cases, MR significantly improved both inclination (P < .001) and version (P < .001). Specifically, MR improved the mean difference from preoperative templates (by 1.9° inclination, 2.4° version), narrowed the SD (by 1.7° inclination, 1.8° version), and decreased the variance (11.7-3.0 inclination, 14.9-4.3 version). A scatterplot of the data demonstrates a concentration of MR cases within 5° of plan relative to SI cases typically within 10° of plan. There was no difference in operative time.

CONCLUSION

MR improved the accuracy and precision of glenoid positioning. Although it is unlikely that 2° makes a detectable clinical difference, our results demonstrate the potential ability for technology like MR to narrow the bell curve and decrease the outliers in glenoid placement. This will be particularly relevant as MR and other similar technologies continue to evolve into more effective methods in guiding surgical execution.

Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty

BACKGROUND

Accurate and precise templating is paramount for anatomic total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RSA) to enhance preoperative planning, streamline surgery, and improve implant positioning. We aimed to evaluate the predictive potential of readily available patient demographic data in TSA and RSA implant sizing, independent of implant design.

METHODS

A total of 578 consecutive, primary, noncemented shoulder arthroplasty cases were retrospectively reviewed. Demographic variables and implant characteristics were recorded. Multivariate linear regressions were conducted to predict implant sizes using patient demographic variables.

RESULTS

Linear models accurately predict TSA implant sizes within 2 millimeters of humerus stem sizes 75.3% of the time, head diameter 82.1%, head height 82.1%, and RSA glenosphere diameter 77.6% of the time. Linear models predict glenoid implant sizes accurately 68.2% and polyethylene thickness 76.6% of the time and within one size 100% and 95.7% of the time, respectively.

CONCLUSION

Linear models accurately predict shoulder arthroplasty implant sizes from demographic data. No significant statistical differences were observed between linear models and machine learning algorithms, although the analysis was underpowered. Future sufficiently powered studies are required for more robust assessment of machine learning models in predicting primary shoulder arthroplasty implant sizes based on patient demographics.