Abstract
<title>Abstract</title> <p>Background: Accurate component implantation is fundamental to the success of reverse shoulder arthroplasty (RSA). Although computer-assisted navigation and robotic technologies have improved the precision of glenoid component placement, most currently available systems provide limited support for the remaining stages of the procedure, particularly humeral preparation and stem implantation. To address these limitations, we developed a workflow-integrated robotic platform implementing a digital surgical workflow that supports the complete surgical process of RSA, including preoperative planning, patient registration, robotic guidance, implant positioning, and postoperative evaluation. This study evaluated the technical feasibility and plan-to-actual accuracy of the system in a cadaveric model. Methods: Four fresh-frozen adult cadavers (eight shoulders) underwent robot-assisted RSA using an investigational shoulder arthroplasty robotic platform jointly developed by Beijing Jishuitan Hospital and Estun (Beijing) Medical Technology Co., Ltd. Preoperative computed tomography datasets were processed using an automated planning platform incorporating deep learning-based segmentation, 3D reconstruction, anatomical landmark identification, and implant planning. The robotic platform provided navigated assistance for humeral osteotomy, glenoid preparation, guidewire placement, baseplate implantation, screw fixation, and humeral stem insertion. Postoperative CT scans were registered to the preoperative planning models using 3D Slicer to quantify deviations between planned and achieved implant positions. Primary outcome measures included glenoid baseplate inclination error, version error, and center-position deviation. Secondary outcomes included humeral stem version and implantation-depth errors. Results: All eight procedures were successfully completed without intraoperative device failure or conversion to conventional techniques. Across all specimens, mean ± standard deviation values were 6.21 ± 3.14° for glenoid inclination, 8.25 ± 5.97° for glenoid version, 2.93 ± 1.59 mm for glenoid center position, 5.39 ± 3.29° for humeral stem version, and 2.63 ± 1.14 mm for humeral stem implantation depth. Implant positioning accuracy improved progressively during sequential procedures, with substantially reduced variability after the initial cadaver, suggesting an initial system familiarization. Following system familiarization, mean glenoid inclination and version errors were 5.02 ± 2.31° and 5.40 ± 3.13°, respectively, demonstrating reproducible implant positioning comparable to the performance reported for established navigation-assisted RSA systems. Conclusions: This cadaveric study demonstrates the feasibility of a workflow-integrated robotic platform capable of supporting multiple stages of RSA within a unified navigated environment. The system achieved reproducible plan-to-actual implant accuracy while facilitating comprehensive surgical workflow integration beyond isolated glenoid guidance. Although further optimization and prospective clinical validation are required, these findings provide preliminary evidence supporting the translational potential of this robotic platform for precision shoulder arthroplasty.</p>