Robotic assistance improves reproducibility of planned glenoid baseplate inclination in reverse shoulder arthroplasty.

Authors

Dilpreet Singh, Meeran Hamawandi, Mariano E Menendez

Published in

International orthopaedics. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

Accurate glenoid baseplate inclination is an important technical goal in reverse shoulder arthroplasty (RSA), but reliably achieving the planned target remains challenging. We compared deviation from planned baseplate inclination between conventional and robotic-assisted RSA using the Mako robotic system (Stryker, Kalamazoo, MI).
We retrospectively reviewed consecutive primary RSAs performed by a single fellowship-trained shoulder surgeon over one year. All cases were planned using Blueprint software (Stryker). Achieved inclination was measured on standardized postoperative radiographs using the RSA angle by two blinded reviewers and averaged for analysis. The primary outcome was absolute deviation from planned inclination. Secondary outcomes included variability in inclination error, the proportion of cases within 5° and 10° of plan, and outliers greater than 10°.
A total of 103 RSAs were included (42 conventional, 61 robotic-assisted). Baseline characteristics were comparable except for body mass index (BMI). Mean absolute deviation was lower with robotic assistance but did not reach significance (2.7° ± 2.1° vs 3.9° ± 3.7°; P = .07). Robotic assistance significantly reduced variability in inclination error (SD 2.1° vs 3.7°; Levene P = .02, F test P < .001). No robotic case deviated more than 10° from plan, compared with 4 of 42 conventional cases (0% vs 10%; P = .03).
In a single-surgeon series, conventional and robotic-assisted RSA achieved similar mean radiographic baseplate inclination accuracy, but robotic assistance reduced error variability and eliminated large outliers. These findings suggest that the value of robotic assistance may lie not in marginal improvements in average accuracy, but in narrowing the error distribution and improving the reproducibility of component placement.

PMID:
42507191
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

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