Laser scanning registration for knee arthroplasty.

Aims: Accurate registration of anatomy is fundamental to robotic and computer-assisted surgery; however, conventional methods rely on manual data acquisition performed by the surgeon using a tracked probe that is slow and affected by operator variability. Limitations of manual methods have hindered wider adoption of surgical navigation despite its benefits for alignment accuracy, particularly in orthopaedic applications. This study evaluates a laser surface scanning system, LumaScan, against standard intraoperative probe-based registration with a controlled phantom knee model. Methods: Surgeons performed repeated probe-based data acquisition for registration, while LumaScan executed operator-initiated scans mounted to a robotic arm. The accuracy of each modality was assessed using root mean square (RMS) error relative to a CT-derived reference model, alongside evaluations of repeatability and acquisition time. Results: LumaScan achieved a RMS error of 0.07 mm (SD 0.02), compared to probe-based methods, which resulted in a RMS error of 0.34 mm (SD 0.36). Acquisition time was reduced from 112 seconds (SD 75.8) manually to 6.38 seconds (SD 0.03) for LumaScan (p < 0.001), with probe-based timing increasing three times from the fastest to the slowest participant despite a fixed protocol. The errors of the femur and tibia achieved by LumaScan had a > 70% reduction compared to manual surface acquisition, with these results being statistically significant (p < 0.001). The results demonstrate that robotic laser scanning can significantly improve speed, accuracy, and precision over manual methods. Minimizing operator influence and standardizing registration has the potential to reduce error sources that persist in clinical workflows, subsequently accelerating the integration of navigation and robotics into routine orthopaedic procedures. Conclusion: LumaScan enables faster, more accurate, and more precise anatomical registration than manual methods.