The field of joint surgery has seen significant advancements in recent years, with the advent of surgical robots. These robots have been used to assist in a range of joint replacement procedures, such as THA, TKA, and UKA. Reports suggest that the use of robotic assistance in TKA can significantly reduce postoperative opioid use, improve limb alignment, and minimize surgical complications. These benefits have led to a rapid increase in the number of RA-TKA procedures being performed globally[13].
Dissatisfaction rates of up to 20% in C-TKA patients[14, 15] emphasize the need for improved surgical techniques. Poor implant positioning, limb alignment, and soft tissue imbalance are the main reasons for this high dissatisfaction rate[16]. RA-TKA can address these issues by providing precise surgical planning and intraoperative navigation, which leads to better postoperative outcomes. RA-TKA also helps to overcome the limitations of inexperienced surgeons, making it possible to achieve the MA concept, KA concept, or even FA concept. By adopting these concepts, RA-TKA can achieve excellent limb alignment and prosthesis positioning, leading to higher patient satisfaction rates[4]. The current accepted MA concept seeks to restore the patient's mHKA angle to 0 ± 3°. Commercially available MAKO, NAVIO, and ROSA robots have been shown to achieve postoperative mHKA angles with a mean ranging from 0.55° (SD 1.9°) to 1.2° (SD 1.1°)[7, 17, 18].
Recently, a new HURWA surgical robotic system received NMPA certification. Zheng et al[9] conducted a study on 73 patients who underwent HURWA RA-TKA, and the results were promising. They were able to achieve a postoperative mHKA of 1.801 ± 1.608°, with a neutral alignment rate of 81.2%, which is consistent with the MA concept.
The learning curve is an important factor to consider when implementing a new surgical technique, as it reflects the number of cases a surgeon needs to perform to become proficient in the procedure. Previous studies have reported different learning curves for various robotic systems, ranging from 6 to 11 cases for the ROSA robot[19] to 7 cases for the image-based MAKO system[6]. The learning curve for the HURWA robot, an image-based semiautonomous robotic arm with an open platform, has not yet been reported.
It is known from the literature that the learning curve of other models of surgical robots is generally less than 20 cases[4], so to analyze the learning curve for RA-TKA with the HURWA robot, this study examined the first 30 consecutive cases performed by three surgeons with varying levels of experience. The primary objective was to determine whether the HURWA robot has a learning curve in terms of operative time, osteotomy accuracy, and limb alignment.
The results of this study showed that there was a learning curve for the use of the HURWA robot in RA-TKA, with experienced surgeons taking less time during the bone-cutting phase. However, there was no significant difference in operative time, osteotomy accuracy, or limb alignment between the three surgeons, suggesting that robotic assistance could help overcome the experience barrier and enable more surgeons to perform TKA. Overall, these findings highlight the potential of robotic systems such as HURWA to improve surgical outcomes and expand access to joint replacement procedures.
From the results, it can be seen that surgeons can reach a stage of proficiency in operative time with 10–20 procedures without relying on traditional surgical experience. The operative time for the three operators was significantly shorter after 20 cases than before but was still longer than that in C-TKA. This is a common phenomenon because RA-TKA involves processes such as mounting the positioning frame, bone registration, and robotic arm positioning and operation, which make the operation time longer. At the same time, the disadvantages of a longer tourniquet time and longer incision exposure cannot be ignored. By continuously optimizing the operating procedure and gradually becoming more proficient, it is expected that the operating time of RA-TKA will be reduced to a level close to that of C-TKA. As the error control mechanism of the robotic arm and the safety line protection mechanism are independent of the operator, the prosthesis position and lower limb alignment are highly reproducible both between and within operators, and can be considered proficient from the beginning. This has important implications for the dissemination of TKA surgical techniques at the primary level.
It should be noted that by plotting a linear regression curve of the average bone-cutting error in this study, it was found that the average error in RA-TKA increased by 0.0052 mm for each case, which may indicate a systematic error in the robot. As a result, it is recommended that the robot be recalibrated after a certain number of procedures.
This study has some limitations that need to be considered. First, the sample size was relatively small and further cases are needed to validate the HURWA system. Second, it was difficult to ensure that all patients received standard postoperative full-leg radiographs. Approximately 1/3 of the patients had knee flexion or rotation due to pain or other reasons, which may have affected the postoperative assessment of limb alignment. Additionally, it is important to note that the HURWA surgical system should not be limited to the MA concept of RA-TKA, and the surgical outcomes of the KA concept of individualized osteotomy should also be explored in subsequent studies.
In conclusion, the HURWA system is a safe and precise system for total knee arthroplasty that can significantly reduce postoperative alignment abnormalities under the MA concept, with minimal reliance on the experience of the C-TKA procedure. However, additional long-term follow-up is necessary to validate its efficacy, and further studies are needed to explore potential additional benefits.