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Purpose <p>Currently, there is no widely accepted method for measuring soft-tissue laxity and defining a balanced total knee arthroplasty (TKA). We aim to evaluate whether robotic technology can facilitate the categorization of intraoperative knee laxity.</p> Methods <p>Our study was conducted in two phases. A senior surgeon performed imageless robotically assisted TKAs (ra-TKAs) using functional alignment. The first phase included 120 patients. Following the surgical approach, the medial and lateral soft-tissue laxity was recorded in extension and 90° flexion. The distribution of the difference and sum of laxities in extension and 90° flexion was assessed to classify laxity phenotypes. The second phase validated the classification in 102 additional ra-TKAs. Laxity phenotypes were evaluated at the start and end of the procedure.</p> Results <p>Laxity difference followed a normal distribution, facilitating categorization into three groups, with a standard deviation of 2.5&#xa0;mm. Three categories of mediolateral laxity severity difference were established: &lt; 2.5&#xa0;mm, 2.5-5&#xa0;mm, and &gt; 5&#xa0;mm. These laxity groups were coded in extension as 1, 2, and 3 and in flexion as A, B, and C, respectively. Nine laxity phenotypes emerged from the combination of the extension and flexion categories (1A-C, 2A-C, and 3A-C). Phenotypes 1A and 1B were the most common at the operation’ beginning, while phenotypes 3B and 1C were the rarest. At the end of the operation, 93% were categorized as class 1A and 1B, defining the “balanced area”.</p> Conclusion <p>Our study recognized nine intraoperative soft-tissue knee laxity phenotypes, potentially laying the groundwork for a surgical consensus on knee balancing.</p>

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Robotic evaluation of articular laxity (REAL) classification: a new intraoperative knee soft-tissue laxity classification using ROSA robotic software

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  • 发表日期：

    2025年3月29日

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  • Eustathios Kenanidis,
  • Nikolaos Milonakis,
  • Alexandros Maslaris,
  • Eleftherios Tsiridis

摘要

Purpose

Currently, there is no widely accepted method for measuring soft-tissue laxity and defining a balanced total knee arthroplasty (TKA). We aim to evaluate whether robotic technology can facilitate the categorization of intraoperative knee laxity.

Methods

Our study was conducted in two phases. A senior surgeon performed imageless robotically assisted TKAs (ra-TKAs) using functional alignment. The first phase included 120 patients. Following the surgical approach, the medial and lateral soft-tissue laxity was recorded in extension and 90° flexion. The distribution of the difference and sum of laxities in extension and 90° flexion was assessed to classify laxity phenotypes. The second phase validated the classification in 102 additional ra-TKAs. Laxity phenotypes were evaluated at the start and end of the procedure.

Results

Laxity difference followed a normal distribution, facilitating categorization into three groups, with a standard deviation of 2.5 mm. Three categories of mediolateral laxity severity difference were established: < 2.5 mm, 2.5-5 mm, and > 5 mm. These laxity groups were coded in extension as 1, 2, and 3 and in flexion as A, B, and C, respectively. Nine laxity phenotypes emerged from the combination of the extension and flexion categories (1A-C, 2A-C, and 3A-C). Phenotypes 1A and 1B were the most common at the operation’ beginning, while phenotypes 3B and 1C were the rarest. At the end of the operation, 93% were categorized as class 1A and 1B, defining the “balanced area”.

Conclusion

Our study recognized nine intraoperative soft-tissue knee laxity phenotypes, potentially laying the groundwork for a surgical consensus on knee balancing.