Full Motion Arc, Laxity-Based Planning for Total Knee Arthroplasty: The Next Milestone for Enabling Technology

Background: Although numerous technologies show promise to improve total knee arthroplasty outcomes, a need to further advance these solutions remains. An area for potential improvement is intraoperative efficiency by reducing the number of required surgeon and software interactions. Therefore, the purpose of the study was to compare the number of surgeon and software interactions for 9 surgical steps between a laxity-based algorithm and a historical algorithm. Methods: Technical logs of 313 total knee arthroplasties performed by 6 surgeons were reviewed. Nine surgical parameters were assessed, including bone cuts and implant positioning. These parameters were compared between the laxity-based algorithm and the historical algorithm cohorts. The number of adjustments from the initial predictive plan to the final plan was calculated for each of the 9 parameters to assess potential improvements in surgical efficiency with the laxity-based algorithm. Results: Overall, the laxity-based algorithm substantially improved the initial planning definition. The mean number of active surgeon click adjustments to the original plan decreased from 13 to 8 clicks. When assessing based on the surgeon, a mean 39% reduction in clicks was noted, ranging from a 19% reduction to a 70% reduction. Conclusions: This study exemplified the potential of an unsupervised learning algorithm in conjunction with a laxity-based device. The laxity-based algorithm substantially reduced the number of active surgeon interactions with the software, which can potentially help reduce cognitive burden and operative time.