MULTI-OBJECTIVE DESIGN OPTIMIZATION OF REVERSE TOTAL SHOULDER ARTHROPLASTY TO MAXIMIZE RANGE OF MOTION AND JOINT STABILITY

Reverse total shoulder arthroplasty was developed to restore range of motion (ROM) and joint stability to patients with pre-operative conditions that are not addressed by conventional replacements. Although reverse total shoulder arthroplasty is the current gold standard for treating a range of indications, the effects of varying its design on functional outcomes of the procedure are still not well understood. To that end, it is not yet clear which configurations, in terms of both design and surgical placement parameters, maximize range of motion and stability of the joint. It was hypothesized that there is trade-off between the two. These types of relationships may be elucidated using multi-objective design optimization to generate a Pareto front. Pareto optimal points represent those where neither performance metric can be further improved without detriment to the other. Multi-objective optimization requires 1) metrics to characterize the objectives to be optimized and 2) an automated computational framework capable of assessing the metrics for any candidate implant design. As such, the pre-cursory goals to performing multi-objective optimization involved the development, validation, and automation of computational tools to predict the performance of reverse should designs with respect to range of motion and joint stability. Characterization of the Pareto front with multi-objective optimization confirmed that there is in fact a trade-off between range of motion and stability. Designs that maximize one functional outcome differ from those that maximize the other. Designs that resulted in intermediate performance in terms of both objectives were variable. This indicates that functional factors other than range of motion and stability, such as mechanical implant stability (fixation) and avoidance of inferior impingement, could serve as deciding factors between implant configurations that achieve similar range of motion and stability results

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