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Clinical Orthopaedics and Related Research®

, Volume 473, Issue 1, pp 206–212 | Cite as

High Degree of Accuracy of a Novel Image-free Handheld Robot for Unicondylar Knee Arthroplasty in a Cadaveric Study

  • Jess H. LonnerEmail author
  • Julie R. Smith
  • Frederic Picard
  • Brian Hamlin
  • Philip J. Rowe
  • Philip E. Riches
Symposium: 2014 Knee Society Proceedings

Abstract

Background

Surgical robotics has been shown to improve the accuracy of bone preparation and soft tissue balance in unicondylar knee arthroplasty (UKA). However, although extensive data have emerged with regard to a CT scan-based haptically constrained robotic arm, little is known about the accuracy of a newer alternative, an imageless robotic system.

Questions/purposes

We assessed the accuracy of a novel imageless semiautonomous freehand robotic sculpting system in performing bone resection and preparation in UKA using cadaveric specimens.

Methods

In this controlled study, we compared the planned and final implant placement in 25 cadaveric specimens undergoing UKA using the new tool. A quantitative analysis was performed to determine the translational, angular, and rotational differences between the planned and achieved positions of the implants.

Results

The femoral implant rotational mean error was 1.04° to 1.88° and mean translational error was 0.72 to 1.29 mm across the three planes. The tibial implant rotational mean error was 1.48° to 1.98° and the mean translational error was 0.79 to 1.27 mm across the three planes.

Conclusions

The image-free robotic sculpting tool achieved accurate implementation of the surgical plan with small errors in implant placement. The next step will be to determine whether accurate implant placement translates into a clinical and functional benefit for the patient.

Keywords

Root Mean Square Error Femoral Component Robotic System Tibial Component Robotic Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank the technicians in the Anatomy Laboratory at Strathclyde University for their help during the project.

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Copyright information

© The Association of Bone and Joint Surgeons® 2014

Authors and Affiliations

  • Jess H. Lonner
    • 1
    Email author
  • Julie R. Smith
    • 2
    • 3
  • Frederic Picard
    • 4
  • Brian Hamlin
    • 5
  • Philip J. Rowe
    • 2
  • Philip E. Riches
    • 2
  1. 1.Department of Orthopaedic Surgery, Rothman InstituteThomas Jefferson UniversityPhiladelphiaUSA
  2. 2.Biomedical Engineering DepartmentUniversity of StrathclydeGlasgowUK
  3. 3.Blue Belt Technologies IncPlymouthUSA
  4. 4.Department of OrthopaedicsGolden Jubilee National HospitalGlasgowUK
  5. 5.The Bone & Joint CenterMagee-Womens Hospital of UPMCPittsburghUSA

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