Related Experiment Video
Updated: Sep 13, 2025
A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
Published on: January 6, 2023
The P Balasubramaniam Award-2024 Singapore Orthopaedic Association Annual Scientific Meeting Award: Novel artificial
Matthew Song Peng Ng1,2, Ryan Wai Keong Loke3, Melvin Kian Loong Tan1,2
1Department of Orthopaedics, National University Hospital, National University Health System, Singapore, Singapore.
Background:
Robotic Total Knee Arthroplasty (rTKA) has become increasingly popular. Intraoperative manual planning of femur and tibia implant positions in all degrees of freedom to achieve surgeon-defined targets and limits of bone cuts, gaps, and alignment is challenging. The final manually defined solution may not be optimal, and surgical duration increases significantly. We aim to demonstrate the effectiveness of our novel algorithm in terms of accuracy and surgical duration.
Methods:
We developed a novel AI computational algorithm to optimize rTKA implant positioning in three-dimensional space. The initial parameters of 3D implant positioning and surgeon-defined target gaps and bone cuts are set. The algorithm determines permutations achieving ideal 3D implant positioning with ± 0.5 mm accuracy, ranking them by surgeon preference and evidence-based criteria. We compared accuracy in achieving surgeon-defined target gaps, intraoperative soft tissue balancing duration, and total surgical time.
Results:
A prospective study of 67 consecutive rTKA patients at a tertiary institution (Nov 2021-Dec 2023) was conducted. 25 patients (mean age 70.4 ± 7.34 years) had our algorithm used intraoperatively, while 42 (mean age 70.5 ± 6.90 years) did not. 92% of rTKAs using our algorithm achieved target gaps ± 1.5 mm, vs. 52% of non-algorithm rTKAs (P = 0.003). The average difference between surgeon-defined target gaps and final achieved gaps was 1.1 ± 0.5 mm in the algorithm group vs. 1.8 ± 1.0 mm in the non-algorithm group (P = 0.003). Soft tissue balancing duration was significantly shorter: 1.16 min ± 0.11 with algorithm use vs. 14.5 min ± 8.3 (P < 0.0001). Total surgical duration was also significantly lower: 38.4 min ± 14.9 vs. 73.7 min ± 19.6 (P = 0.0002).
Conclusion:
Our novel AI algorithm significantly improves accuracy in achieving surgeon-defined target extension and flexion gaps while reducing soft tissue balancing and total surgical duration. This is highly promising for achieving both reproducibility and efficiency in rTKAs. Video Abstract.
