The Accuracy of Three-Dimensional CT Scan Software in Predicting Prosthetic Utilization in Total Shoulder Arthroplasty

Introduction: Recent innovations in shoulder arthroplasty include three-dimensional (3D) CT software imaging that can be used to predict which prosthetic implants will be used intraoperatively. Correct prediction of the implants may optimize supply chain logistics for the surgeon, hospital, ambulatory surgery center, and the implant company. The purpose of this study was to examine a single surgeon's experience with this software to determine its predictive accuracy in determining which implants would be used intraoperatively. Methods: A retrospective review of patients undergoing total shoulder arthroplasty (TSA) performed by a single surgeon was performed. Inclusion criteria were patients undergoing anatomic (aTSA) or reverse (rTSA) TSA examined preoperatively with the 3D CT planning software. A chart review was performed to compare the accuracy of the preoperative plan in predicting the actual prostheses implanted at surgery. Results: Two hundred seventy-eight shoulders from 260 patients were included. One hundred fifty-one shoulders underwent aTSA, and 127 shoulders underwent rTSA. The surgeon was able to predict the type of arthroplasty (anatomic versus reverse) implanted in 269 of 278 (97%) shoulders. Using the 3D CT software, the surgeon was able to predict all the implants implanted in 68 shoulders (24%). For aTSA, 3D CT imaging successfully predicted all implants implanted in 43 shoulders (28%), glenoid implants implanted in 120 of 148 shoulders (81%), and humeral implants implanted in 54 shoulders (36%). For rTSA, 3D CT imaging successfully predicted all implants implanted in 26 shoulders (20%), glenoid implants implanted in 106 shoulders (83%), and humeral implants implanted in 39 shoulders (31%). Conclusions: The 3D CT software combined with surgeon's judgment provided a high accuracy (97%) in determining the type of arthroplasty, a moderately high accuracy in determining the glenoid implants (81% to 83%), a low accuracy in determining humeral implants (31% to 36%), and a low accuracy in determining all prostheses used for each surgery (20% to 28%).