Fukui Daisuke, Nishiyama Daisuke, Yamanaka Manabu, Ueno Takeru, Tonoo Morihiro, Yamada Hiroshi
Department of Orthopaedic Surgery, Wakayama Medical University, Wakayama, Japan.
Department of Orthopaedic Surgery, Kishigawa Rehabilitation Hospital, Kinokawa, Wakayama, Japan.
J Robot Surg. 2025 Sep 5;19(1):566. doi: 10.1007/s11701-025-02709-3.
Dynamic joint balancing (DJB) in robotic-assisted total knee arthroplasty (RATKA) allows surgeons to simulate implant positioning and predict soft tissue balance intraoperatively before bone resection. Although virtual gap (VG) estimation is integral to this process, its accuracy in predicting the final gap (FG) after implantation remains uncertain. We conducted a retrospective analysis of 77 knees in 61 patients undergoing RATKA with the MAKO system. VG was recorded at four positions (medial/lateral in 10° extension and 90° flexion) prior to bone resection. FG was measured post-implantation using a 9-mm trial insert. Correlations between VG and FG were assessed, along with the frequency of VG-FG discrepancies ≥ 3 mm. We further assessed relationships with coronal implant alignment and evaluated the presence of a learning curve. VG and FG were moderately to strongly correlated at all measurement points (r = 0.50-0.71). FG exceeded VG by 0.6-1.3 mm on average, with the largest 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 significant association was found between implant alignment error and VG-FG discrepancy. Logistic regression revealed no learning curve effect for either surgeon. DJB-based VG estimation provides a reliable, reproducible intraoperative reference in RATKA. However, consistent overestimation-particularly in flexion-suggests the need for improved modeling or measurement techniques. DJB's independence from a learning curve supports its value in standardizing soft tissue balancing across surgical experience levels.
机器人辅助全膝关节置换术（RATKA）中的动态关节平衡（DJB）使外科医生能够在骨切除术前模拟植入物定位并预测术中软组织平衡。尽管虚拟间隙（VG）估计是这一过程不可或缺的部分，但其在预测植入后最终间隙（FG）方面的准确性仍不确定。我们对61例接受MAKO系统RATKA手术的患者的77个膝关节进行了回顾性分析。在骨切除术前，于四个位置（10°伸直和90°屈曲时的内侧/外侧）记录VG。使用9毫米试验插入物在植入后测量FG。评估了VG与FG之间的相关性，以及VG - FG差异≥3毫米的频率。我们进一步评估了与冠状面植入物对线的关系，并评估了学习曲线的存在情况。在所有测量点，VG与FG呈中度至高度相关（r = 0.50 - 0.71）。FG平均比VG超出0.6 - 1.3毫米，屈曲外侧间隙的差异最大。间隙误差≥3毫米在屈曲时（8.4%）比伸直时（1.3%）明显更频繁（p = 0.0036）。未发现植入物对线误差与VG - FG差异之间存在显著关联。逻辑回归显示，两位外科医生均未出现学习曲线效应。基于DJB的VG估计在RATKA中提供了可靠、可重复的术中参考。然而，持续高估——尤其是在屈曲时——表明需要改进建模或测量技术。DJB不受学习曲线影响，这支持了其在跨手术经验水平标准化软组织平衡方面的价值。
