Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods

Robotic systems have been shown to improve unicompartmental knee arthroplasty (UKA) component placement accuracy compared to conventional methods when used by experienced surgeons. We aimed to determine whether inexperienced UKA surgeons can position components accurately using robotic assistance wh...

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Main Authors: Monil Karia, Milad Masjedi, Barry Andrews, Zahra Jaffry, Justin Cobb
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Advances in Orthopedics
Online Access:http://dx.doi.org/10.1155/2013/481039
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author Monil Karia
Milad Masjedi
Barry Andrews
Zahra Jaffry
Justin Cobb
author_facet Monil Karia
Milad Masjedi
Barry Andrews
Zahra Jaffry
Justin Cobb
author_sort Monil Karia
collection DOAJ
description Robotic systems have been shown to improve unicompartmental knee arthroplasty (UKA) component placement accuracy compared to conventional methods when used by experienced surgeons. We aimed to determine whether inexperienced UKA surgeons can position components accurately using robotic assistance when compared to conventional methods and to demonstrate the effect repetition has on accuracy. Sixteen surgeons were randomised to an active constraint robot or conventional group performing three UKAs over three weeks. Implanted component positions and orientations were compared to planned component positions in six degrees of freedom for both femoral and tibial components. Mean procedure time decreased for both robot (37.5 mins to 25.7 mins) (P=0.002) and conventional (33.8 mins to 21.0 mins) (P=0.002) groups by attempt three indicating the presence of a learning curve; however, neither group demonstrated changes in accuracy. Mean compound rotational and translational errors were lower in the robot group compared to the conventional group for both components at all attempts for which rotational error differences were significant at every attempt. The conventional group’s positioning remained inaccurate even with repeated attempts although procedure time improved. In comparison, by limiting inaccuracies inherent in conventional equipment, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their experience.
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spelling doaj-art-31ede540e9cf469d8a0871ed334a85e12025-08-20T03:55:03ZengWileyAdvances in Orthopedics2090-34642090-34722013-01-01201310.1155/2013/481039481039Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional MethodsMonil Karia0Milad Masjedi1Barry Andrews2Zahra Jaffry3Justin Cobb4MSK Lab, Department of Orthopaedics, Charing Cross Hospital, Imperial College London, Fulham Place Road, London W6 8RF, UKMSK Lab, Department of Orthopaedics, Charing Cross Hospital, Imperial College London, Fulham Place Road, London W6 8RF, UKMSK Lab, Department of Orthopaedics, Charing Cross Hospital, Imperial College London, Fulham Place Road, London W6 8RF, UKMSK Lab, Department of Orthopaedics, Charing Cross Hospital, Imperial College London, Fulham Place Road, London W6 8RF, UKMSK Lab, Department of Orthopaedics, Charing Cross Hospital, Imperial College London, Fulham Place Road, London W6 8RF, UKRobotic systems have been shown to improve unicompartmental knee arthroplasty (UKA) component placement accuracy compared to conventional methods when used by experienced surgeons. We aimed to determine whether inexperienced UKA surgeons can position components accurately using robotic assistance when compared to conventional methods and to demonstrate the effect repetition has on accuracy. Sixteen surgeons were randomised to an active constraint robot or conventional group performing three UKAs over three weeks. Implanted component positions and orientations were compared to planned component positions in six degrees of freedom for both femoral and tibial components. Mean procedure time decreased for both robot (37.5 mins to 25.7 mins) (P=0.002) and conventional (33.8 mins to 21.0 mins) (P=0.002) groups by attempt three indicating the presence of a learning curve; however, neither group demonstrated changes in accuracy. Mean compound rotational and translational errors were lower in the robot group compared to the conventional group for both components at all attempts for which rotational error differences were significant at every attempt. The conventional group’s positioning remained inaccurate even with repeated attempts although procedure time improved. In comparison, by limiting inaccuracies inherent in conventional equipment, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their experience.http://dx.doi.org/10.1155/2013/481039
spellingShingle Monil Karia
Milad Masjedi
Barry Andrews
Zahra Jaffry
Justin Cobb
Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
Advances in Orthopedics
title Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
title_full Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
title_fullStr Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
title_full_unstemmed Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
title_short Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods
title_sort robotic assistance enables inexperienced surgeons to perform unicompartmental knee arthroplasties on dry bone models with accuracy superior to conventional methods
url http://dx.doi.org/10.1155/2013/481039
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