Evaluation of augmented reality guidance for glenoid pin placement in total shoulder arthroplasty
Year: 2025
Type: article
Abstract: Purpose Computer-aided navigation and patient-specific 3D printed guides have demonstrated superior outcomes in total shoulder arthroplasty (TSA). Nevertheless, few TSAs are inserted using these technologies. Head-worn augmented reality (AR) devices can provide intuitive 3D computer navigation to the surgeon. This study investigates AR navigation in conjunction with adaptive spatial drift correction toward TSA. Methods A phantom study was performed to assess the performance of AR navigated pin placement in TSA. Two medical experts performed a total of 12 pin placements into phantom scapula; six were placed using an end-to-end AR-navigated technique, and six using a common freehand technique. Inside-out infrared (IR) tracking was designed and integrated into the AR headset to correct for device drift and provide tool tracking. Additionally, the impact of IR tool tracking, registration, and superposed/juxtaposed visualization techniques was investigated. Results The AR-navigated pin placement resulted in a mean entry point error of 1.06 mm ± 0.64 mm and directional error of $${1.66^\circ \pm 0.65^\circ }$$ 1 . 66 ∘ ± 0 . 65 ∘ . Compared with the freehand technique, AR navigation resulted in improved directional outcomes ( $$p=0.03$$ p = 0.03 ), while entry point accuracy was not significantly different ( $$p=0.44$$ p = 0.44 ). IR tool tracking error was 1.47 mm ± 0.69 mm and $${0.92^\circ \pm 0.50^\circ }$$ 0 . 92 ∘ ± 0 . 50 ∘ , and registration error was 4.32 mm ± 1.75 mm and $${2.56^\circ \pm 0.82^\circ }$$ 2 . 56 ∘ ± 0 . 82 ∘ . No statistical difference between AR visualization techniques was found in entry point ( $$p=0.22$$ p = 0.22 ) or directional ( $$p=0.31$$ p = 0.31 ) errors. Conclusion AR navigation allowed for comparable pin placement outcomes with those reported in the literature for patient-specific 3D printed guides; moreover, it complements the patient-specific planning without the need for the guides themselves.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,890
OpenAlex ID: W4411209342