2025 ISAKOS Biennial Congress ePoster
Novel Algorithm for Gap Balancing and Bone Cuts in Robotic Total Knee Replacements Significantly Improves Accuracy and Surgical Duration
Zi Qiang Glen Liau , MBBS, MRCS, MMed, FRCS, FAMS, MBA, Singapore SINGAPORE
Matthew Song Peng Ng, MBBS, Singapore, Singapore SINGAPORE
Ryan Wai Keong Loke, MBBS candidate, Singapore SINGAPORE
National University Hospital, National University Health System, Singapore, Singapore, SINGAPORE
FDA Status Not Applicable
Summary
We have developed a novel computational algorithm to achieve optimal positioning of robotic total knee replacement (rTKR) implants in three-dimensional space. It significantly improves the accuracy of achieving the surgeon’s target extension and flexion gaps, while significantly shortening overall surgical duration. This is highly promising for achieving reproducibility and efficiency in rTKRs.
ePosters will be available shortly before Congress
Abstract
Background
Robotic Total Knee Replacements (rTKR) have become increasingly popular in recent years. However, intra-operative manual planning of the positions of the femur and tibia implants in all possible degrees of freedom to achieve the surgeon’s ideal targets and limits of bone cuts, gaps and alignment is challenging. The final manually defined solution may not be the most optimal, and surgical duration becomes extended significantly. The aim of our study is to demonstrate the effectiveness of utilising our novel algorithm clinically in terms of accuracy and surgical duration.
Methods
We have developed a novel computational algorithm to achieve optimal positioning of rTKR implants in three-dimensional space. The initial set of parameters of the 3D positioning of the implants in relation to each other and the surgeon-defined target gaps and bone cuts are defined. The algorithm will then determine various permutations that give the ideal 3D positioning of the implants, to fulfil the targets with an accuracy of ±0.5mm, while also ranking them according to a set of surgeon-preference and evidence-based criteria. We compared the accuracy in achieving surgeon-defined target gaps between both groups, the intra-operative gap-balancing duration, and total surgical duration. Power analysis based on a pilot study showed that 44 patients were required.
Results
A prospective study of 67 consecutive patients who underwent rTKR at a tertiary institution from November 2021 to December 2023 was performed. 25 patients (mean age 70.4 years ±7.34) had our novel algorithm utilised intra-operatively, while 42 patients (mean age 70.5 years ±6.90) did not.
92% of the rTKRs that used our algorithm achieved the surgeon-defined target gaps ±1.5mm, compared to 52% of rTKRs that were done manually (P = 0.003). The average difference between surgeon-defined target gaps and final achieved gaps was 1.08 ±0.51mm in the algorithm group, significantly lower than that in the non-algorithm group, which was 1.81 ±1.04mm (P = 0.003).
Gap-balancing duration was significantly shorter: 1.16min ±0.11 with algorithm use, compared to 14.49min ±8.31 (P < 0.0001). Total surgical duration was also significantly lower with algorithm use, with a mean total surgical time of 38.4min ±14.94 compared to 73.66min ±19.61 (P = 0.0002).
Conclusions
Our novel algorithm for gap-balancing in rTKRs significantly improves both accuracy of achieving the surgeon’s target extension and flexion gaps, along with gap-balancing and overall surgical duration. This is highly promising for achieving both reproducibility and efficiency in rTKRs.