Dynamic joint balancing provides consistent gap prediction without a learning curve in robotic-assisted total knee...

Area of Science:

• Orthopedic Surgery
• Robotics in Medicine
• Biomechanical Engineering

Background:

• Dynamic joint balancing (DJB) is crucial for robotic-assisted total knee arthroplasty (RATKA) to predict soft tissue balance before bone resection.
• Virtual gap (VG) estimation is a key component of DJB, but its accuracy in predicting the final gap (FG) requires further investigation.

Purpose of the Study:

• To evaluate the correlation between intraoperatively measured virtual gaps (VG) and post-implantation final gaps (FG) in RATKA.
• To assess the accuracy of DJB in predicting FG and identify factors influencing discrepancies.
• To investigate the learning curve associated with DJB in RATKA.

Main Methods:

• Retrospective analysis of 77 knees (61 patients) undergoing RATKA using the MAKO system.
• VG measurements recorded at four positions (medial/lateral, 10° extension, 90° flexion) prior to bone resection.
• FG measured post-implantation; VG-FG correlations and discrepancies (≥3 mm) analyzed, alongside implant alignment and learning curve effects.

Main Results:

• Moderate to strong correlations (r=0.50-0.71) observed between VG and FG across all measurement points.
• FG consistently overestimated VG (0.6-1.3 mm average), with larger discrepancies in flexion lateral gaps.
• Gap errors ≥3 mm were significantly more frequent in flexion (8.4%) than extension (1.3%) (p=0.0036); no association with alignment error or learning curve.

Conclusions:

• DJB-based VG estimation offers a reliable intraoperative reference in RATKA, independent of surgical experience.
• Overestimation of FG, particularly in flexion, highlights the need for improved DJB modeling or measurement techniques.
• DJB's consistency supports its role in standardizing soft tissue balancing in total knee arthroplasty.