Efficacy study of robot-assisted total knee arthroplasty in patients with different levels of medical experience

Abstract

This study aims to explore whether RA-TKA can effectively bridge the gap in surgical accuracy and efficacy caused by differences in the original experience of surgeons through its “low precision learning curve” characteristic. A retrospective analysis was conducted on 100 patients with knee osteoarthritis who underwent total knee arthroplasty from January 2023 to December 2024. The patients were divided into four groups based on the surgeon and the type of surgery: low-experience doctors performed CTKA, low-experience doctors performed RA-TKA, high-experience doctors performed CTKA, and high-experience doctors performed RA-TKA. The main evaluation indicators included operation time, incision length, intraoperative blood loss, imaging accuracy (HKA angle, FFC, FTC, LTC), and early clinical outcomes (KSS score, joint range of motion ROM, pain VAS score). In terms of surgical efficiency, the total operation time and incision length of the low-experience doctor group (Group A) were longer than those of the high-experience doctor group (Group B) (P < 0.05). Within the groups, regardless of the doctor’s experience, the operation time and incision length of the RA-TKA group were longer than those of the same group’s CTKA group, but the intraoperative blood loss was reduced (P < 0.05). However, within the RA-TKA group, there were no significant differences in operation time, incision length, and blood loss between the high-experience and low-experience doctors (P > 0.05); while in the CTKA group, the operation time and incision length of the low-experience doctors were significantly higher than those of the high-experience doctors (P < 0.05). In terms of surgical accuracy, the imaging indicators (HKA, FFC, FTC, LTC) of the patients in Group B were overall better than those in Group A (P < 0.05). Within the groups, in the low-experience doctor group (Group A), RA-TKA was significantly better than CTKA in all measurement indicators (HKA, FFC, FTC, LTC) (P < 0.01); in the high-experience doctor group (Group B), RA-TKA was only better than CTKA in the HKA angle (P < 0.05). The key finding was that in the CTKA group, high-experience doctors were significantly better than low-experience doctors in all imaging indicators (P < 0.05), but in the RA-TKA group, there were no statistical differences in all precision indicators between the high-experience and low-experience doctors (P > 0.05). In terms of clinical function, at 3 days after surgery, the KSS and ROM of the A-RA-TKA group were significantly better than those of the A-CTKA group, and the VAS pain score was lower (P < 0.05). At 1 year after surgery, this advantage still existed in KSS and ROM (P < 0.05). For high-experience doctors, robot-assisted surgery improved KSS at 3 days after surgery and ROM at 1 year. For low-experience doctors (A-RA-TKA), the clinical function results at the same time points were mostly not significantly different from those of high-experience doctors (B-RA-TKA) (P > 0.05, except for a slight but significant statistical difference in VAS at 1 year). RA-TKA can effectively improve the surgical accuracy of low-experience doctors, enabling them to reach the same level as high-experience doctors in the early application stage.

Introduction

Total Knee Arthroplasty (TKA) is the main surgical approach for treating end-stage knee osteoarthritis. Its success relies on the accurate implantation of the prosthesis and the restoration of the lower limb alignment. In the traditional surgical model, the personal experience and manual skills of the doctor are the core factors determining the quality of the surgery, which directly leads to significant differences in the incidence of abnormal prosthesis alignment among doctors of different qualifications, especially young physicians, and may affect the long-term efficacy [1].

The emergence of robot-assisted TKA (robot-assisted total knee arthroplasty, RATKA) provides a new solution to improve the predictability and repeatability of the surgery through preoperative three-dimensional digital planning and active control or precise navigation of the robotic arm. A large amount of evidence indicates that RATKA is superior to traditional methods in improving the alignment of the lower limb and the accuracy of prosthesis positioning [2]. With the popularization of the technology, all surgeons are facing the challenge of mastering this new skill, and the learning curve - the process from beginning to reach a stable and proficient level - has become the core concern in clinical practice.

It is worth noting that existing studies suggest that the precision learning curve and efficiency learning curve of RATKA may be separated. Multiple reports indicate that although doctors need more time to adapt to the process initially, the accuracy of prosthesis implantation often remains at a high level from the beginning [3]. For example, studies using an imageless navigation system show that surgeons maintain stable surgical accuracy while reaching the proficient operation time [4]. This phenomenon raises an extremely practical question: Can RATKA, with its “low precision learning curve” characteristic, effectively bridge the precision gap caused by the original experience differences of doctors, thereby providing a “high starting point” technical platform for young physicians? Currently, there are no targeted relevant research reports. Therefore, we conducted this retrospective study to systematically explore: (1) the influence of experience on precision in traditional TKA; (2) the improvement effect of RATKA on the surgical precision of junior doctors; (3) the efficiency differences among doctors of different qualifications when using RATKA. The results are presented as follows.

Materials and methods

Study subjects and grouping

Inclusion and exclusion criteria

Inclusion criteria: ① Age 65–75 years old; ② Primary knee osteoarthritis (Kellgren-Lawrence grade III-IV); ③ First unilateral total knee arthroplasty (TKA); ④ Knee valgus 0–15° and AORI Type I; ⑤ Complete clinical data and completed relevant follow-up.

Exclusion criteria: ① History of knee surgery; ② Active infection or inflammatory joint disease; ③ Severe bone defect; ④ Severe systemic disease; ⑤ Neuromuscular disorder; ⑥ BMI > 35 kg/m².

Doctor qualifications and training

1 junior doctor: 5 years or less of joint surgery experience, and the number of primary TKAs performed by the lead surgeon before RA-TKA was less than 100 cases.

1 senior doctor: 15 years or more of joint surgery experience, and the number of primary TKAs performed by the lead surgeon before RA-TKA was more than 1000 cases.

All participating doctors completed the manufacturer’s standardized training for the used robot system (Huawei HURWA) (the first 5 RA-TKAs performed). This setting ensures that the surgeon and the surgical team have an initial adaptation to the robot. All research cases were handled by the same group of 3 orthopedic physicians and 2 senior specialist nurses as fixed assistants to control for the confounding effect of assistant experience [5].

Clinical data

This study retrieved cases of knee osteoarthritis that underwent total knee arthroplasty at our center from January 2023 to December 2024. According to the inclusion and exclusion criteria, 25 patients from each group were selected from the case database in chronological order:

• A-CTKA group: junior doctors + traditional TKA (25 cases of CTKA before RA-TKA);

• A-RA-TKA group: junior doctors + RA-TKA (25 consecutive cases of RA-TKA);

• B-CTKA group: senior doctors + traditional TKA (25 cases of CTKA before RA-TKA);

• B-RA-TKA group: senior doctors + RA-TKA (25 consecutive cases of RA-TKA).

Surgery and perioperative management

All surgeries were performed under general anesthesia combined with nerve block, with the use of tourniquets. The traditional TKA group adopted standard intramedullary/extra-medullary positioning and gap balance techniques. The RA-TKA group followed the standard procedure of the system: preoperative CT three-dimensional planning, intraoperative optical tracker registration, and mechanical arm-assisted osteotomy. After the osteotomy was completed and the gap balance was achieved, pulse irrigation of the surgical area was performed, followed by the implantation of the prosthesis, 2 − 0 barbed sutures were used to suture the joint capsule, 1 − 0 barbed sutures were used to suture the subcutaneous tissue, skin staples were used to close the incision, and pressure bandaging of the knee joint with dressings was applied. The surgery was concluded.

Note

The HURWA system is based on preoperative hip-knee-ankle CT. Traditional TKA uses femoral intramedullary positioning + tibial extra-medullary positioning. The alignment strategy for all cases of the knee joint was mechanical alignment (target HKA was 180°), and the balance of the inside and outside of the joint and flexion-extension relied on joint release. When the lateral and medial gaps were significantly abnormal, the HKA was allowed to deviate from the target by 3° or less. All cases used the Aikang PS type prosthesis.

All patients were managed according to the same enhanced recovery after surgery (ERAS) pathway: ① Antifibrinolytic regimen: 2 g tranexamic acid was intravenously infused before the operation, 3 g was infused into the joint cavity after joint capsule suture, and 1 g was intravenously infused again at 3 h, 6 h, and 24 h after the operation; ② Anticoagulation regimen: rivaroxaban was orally administered 10 mg daily for 30 days; ③ Infection prevention: cefuroxime sodium 0.75 g was intravenously infused within 24 h after the operation; ④ Analgesic regimen: 0.1 g celecoxib was taken orally before and after the operation, and oxycodone was added when the pain was unbearable after the operation; intraoperative local injection of ropivacaine 100 mg + compound betamethasone 7 mg + 40 ml normal saline was performed; ⑤ Rehabilitation regimen: professional rehabilitation therapists guided knee joint functional exercises on the first day after the operation, and patients were instructed to return to the hospital for follow-up visits regularly after discharge.

Evaluation indicators

Primary endpoint

Lower Limb Alignment: Hip-Knee-Ankle Angle (HKA), with the target being 180°, the proportion of patients whose HKA angle is within ± 3° after surgery is regarded as the primary imaging endpoint [9].

Secondary endpoints

Imaging: The proportion of FFC, FTC, and LTC within ± 3° (target 90°); the mean absolute deviation from the target value for each angle. Clinical Function: KSS, VAS, and ROM at 3 days after surgery (early rehabilitation) and 1 year after surgery (long-term efficacy).

Surgical comparison

Surgical Efficiency: Total operation time (from incision to suture), robot-specific operation time (from installing the tracer to completing the robot-assisted osteotomy).

Perioperative Indicators: Incision length, intraoperative blood loss (weighing method using gauze and suction device).

Complications: ① Intraoperative popliteal artery injury, postoperative foot drop (peroneal nerve injury); ② Postoperative incision infection or exudation for more than 5 days; ③ Venous thrombosis in the affected limb within 1 week after surgery (all cases underwent lower limb vascular color Doppler ultrasound复查 1 week after surgery); ④ Knee lateral DR at 1 day after surgery suggesting anterior cortical depression (NOTCH) of the femur༛ ⑤ Reoperation rate within 1 year due to infection, loosening, stiffness, pain, etc.

Postoperative DR images were measured by two independent orthopedic physicians who were blinded to the groups. To evaluate the measurement reliability, 20 sets of complete images of patients were randomly selected, and two physicians conducted two measurements 2 weeks apart. The intraclass correlation coefficient (ICC, two-factor random effects model, absolute consistency) was used to evaluate the inter-observer and intra-observer reliability. The results showed that the inter-observer ICC of all imaging angles (HKA, FFC, FTC, LTC) was 0.92–0.98, and the intra-observer ICC was 0.95–0.99, indicating excellent reliability of the measurement. The final results were the average of the first measurements by the two physicians for statistical analysis.

Statistical analysis

Statistical analysis was conducted using SPSS 26.0 software. For continuous variables, the Shapiro-Wilk test was used to assess normality. All variables were found to follow a normal distribution. Categorical variables were expressed as frequencies (percentages). For age, BMI, preoperative HKA angle, operation time, robot-specific time, incision length, and intraoperative blood loss, these measurement data were presented as (x̅ ± s), and comparisons between groups were performed using the independent sample *t* test. For the proportion of patients with a deviation of ≤ 3° in the postoperative hip-knee-ankle angle (HKA) from the target value of 180°, as a binary variable, the risk difference (RD) and its 95% confidence interval (CI) were analyzed. For comparisons of continuous variables (angle deviation values, KSS, VAS, ROM), the mean difference (MD) and its 95% CI were reported along with the x̅ ± s and P values. Among these, KSS, VAS, and ROM at different time points (3 days post-operation, 1 year) were evaluated using repeated measures variance analysis. For comparisons of categorical variables (such as gender, K-L classification, complication rate), the chi-square test was used; when the expected frequency in any cell was < 5, the Fisher’s exact probability method was employed. To control the Type I error inflation caused by multiple comparisons, the significance level for multiple secondary imaging endpoints (FFC, FTC, LTC outlier ratio) was corrected using Bonferroni (corrected α = 0.017). The significance level (α) was set at 0.05, and a two-sided P < 0.05 was considered statistically significant.

Results

Baseline data and general surgery information

A total of 100 patients were included in the study and completed the follow-up. The baseline data were balanced and comparable (Table 1).

Table 1.

Comparison of patient baseline data (x̅ ± s)

	Group A (n = 50)	Group B (n = 50)	t/χ²	P
Age (years, x̅ ± s)	67.55 ± 7.15	67.4 ± 7.65	0.102	0.919
RA-TKA group (n = 25)	67.9 ± 6.8	67.0 ± 7.2	0.467	0.642
CTKA group (n = 25)	67.2 ± 7.5	67.8 ± 8.1	0.293	0.771
Within-group P	0.711	0.678
Gender (male/female)	20/30	20/30	0.000	1.000
RA-TKA group (n = 25)	10/15	11/14	0.067	0.796
CTKA group (n = 25)	10/15	9/16	0.186	0.667
Within-group P	1.000	0.796
BMI (kg/m², x̅ ± s)	25.9 ± 2.55	25.4 ± 2.55	1.000	0.320
RA-TKA group (n = 25)	26.0 ± 2.5	25.5 ± 2.7	0.714	0.478
CTKA group (n = 25)	25.8 ± 2.6	25.3 ± 2.4	0.769	0.445
Within-group P	0.767	0.753
K-L grade (III/IV)	22/28	23/27	0.040	0.841
RA-TKA group (n = 25)	10/15	13/12	0.463	0.496
CTKA group (n = 25)	12/13	11/14	0.071	0.789
Within-group P	0.796	0.796
Preoperative HKA (°, x̅ ± s)	173.05 ± 4.25	172.8 ± 3.95	0.300	0.765
RA-TKA group (n = 25)	173.3 ± 4.2	172.5 ± 4.0	0.714	0.478
CTKA group (n = 25)	172.8 ± 4.3	173.1 ± 3.9	0.273	0.786
Within-group P	0.663	0.550

Surgical-related indicators

The total operation time and incision length of patients in Group A were longer than those in Group B, and the difference was statistically significant (P < 0.05); however, there was no statistically significant difference in intraoperative blood loss (P > 0.05). Within the groups, patients in the RA-TKA group had longer operation time and incision length compared to those in the CTKA group, and less intraoperative blood loss, with statistically significant differences (P < 0.05); but there was no statistically significant difference in the comparison of operation time, incision length, and intraoperative blood loss between the two groups of doctors in the RA-TKA group (P > 0.05); in the CTKA group, the operation time and incision length of junior doctors were longer than those of senior doctors, with statistically significant differences (P < 0.05) (see Table 2).

Table 2.

Comparison of Surgical-related indicators (x̅ ± s)

	Group A (n = 50)	Group B (n = 50)	t/χ²	P
Operation time (min, x̅ ± s)	116.05 ± 21.5	104.55 ± 24.3	2.48	0.015
RA-TKA group (n = 25)	136.5 ± 8.3	130.8 ± 7.1	2.65	0.011
CTKA group (n = 25)	95.6 ± 9.2	78.3 ± 8.5	6.96	< 0.001
Within-group P	< 0.001	< 0.001
Robot-exclusive time (min, x̅ ± s)	73.8 ± 13.1	71.5 ± 12.2	0.66	0.514
Incision length (cm, x̅ ± s)	15.05 ± 1.0	14.4 ± 1.3	2.88	0.005
RA-TKA group (n = 25)	15.9 ± 0.5	15.8 ± 0.6	0.64	0.526
CTKA group (n = 25)	14.2 ± 0.9	13.0 ± 0.7	5.24	< 0.001
Within-group P	< 0.001	< 0.001
Intraoperative blood loss (ml, x̅ ± s)	106.95 ± 42.8	100.45 ± 44.4	0.73	0.468
RA-TKA group (n = 25)	75.6 ± 48.3	71.2 ± 50.1	0.31	0.759
CTKA group (n = 25)	138.3 ± 35.2	129.7 ± 38.6	0.82	0.416
Within-group P	< 0.001	< 0.001

Primary endpoint: proportion of outliers in HKA ( > ± 3°)

The primary endpoint was the proportion of outliers in HKA deviating by more than 3° from the target. Using robot-assisted surgery (A-RA-TKA) by junior doctors significantly reduced the incidence of outliers (RD + 40%, 95% CI: +18% to + 62%; P < 0.001), and their accuracy (92%) reached a comparable level to that of senior doctors’ robot-assisted surgery (B-RA-TKA, 96%) (RD -4%, 95% CI: -18% to + 10%; P = 0.642). In traditional surgery, the incidence of outliers by junior doctors tended to be higher than that of senior doctors (60% vs. 80%, RD -20%, 95% CI: -42% to + 2%; P = 0.076) (see Table 3).

Table 3.

Comparison of the proportion of patients with HKA within the target range (177°-183°) in each group after surgery

	n	HKA within ± 3° (n, %)	Compared with the CTKA group of the same doctor (RD, 95% CI)	P
Group A (Junior Doctors)	50	38 (76%)	-	-
A-RA-TKA group	25	23 (92%)	+ 40% (+ 18% to + 62%)	< 0.001
A-CTKA group	25	15 (60%)	-	-
Group B (Senior Doctors)	50	44 (88%)	-	-
B-RA-TKA group	25	24 (96%)	+ 16% (-4% to + 36%)	0.157
B-CTKA group	25	20 (80%)	-	-
Comparison between groups (RA-TKA)	-	-	A-RA-TKA vs. B-RA-TKA: RD -4% (-18% to + 10%)	0.642
Comparison between groups (CTKA)	-	-	A-CTKA vs. B-CTKA: RD -20% (-42% to + 2%)	0.076

Secondary endpoints: proportion of absolute deviations from target values and outliers

In the secondary imaging endpoints, robot-assisted surgery also significantly improved the alignment accuracy of junior doctors in FFC, FTC, and LTC, as evidenced by a significant reduction in absolute deviation values (all P < 0.001) and a significant decrease in the proportion of outliers (all P < 0.017). For senior doctors, robot-assisted surgery further optimized the accuracy of HKA and LTC (P < 0.05), and there was no significant difference in other indicators compared to traditional surgery. Most importantly, when using robot-assisted surgery, there were no statistically significant differences in all imaging accuracy indicators between junior and senior doctors (all P > 0.05) (see Table 4).

Table 4.

Proportion of absolute deviations from target values and outliers (> ± 3°) in postoperative prosthesis alignment angles (x̅ ± s)

Indicators and Groups	Absolute deviation value (absolute value, °)	MD (95% CI) vs. Group of the doctor’s CTKA	Proportion of outliers (> 3°)	RD (95% CI) vs. Group of the doctor’s CTKA†
HKA
A-RA-TKA (n = 25)	1.5 ± 1.0	-1.8 (-2.5 to -1.1)***	2/25 (8%)	-32% (-52% to -12%)**
A-CTKA (n = 25)	3.3 ± 2.2	-	10/25 (40%)	-
B-RA-TKA (n = 25)	1.2 ± 0.9	-0.8 (-1.4 to -0.2)*	1/25 (4%)	-16% (-36% to + 4%)
B-CTKA (n = 25)	2.0 ± 1.5	-	5/25 (20%)	-
FFC
A-RA-TKA (n = 25)	1.1 ± 0.8	-2.1 (-2.9 to -1.3)***	0/25 (0%)	-36% (-56% to -16%)**
A-CTKA (n = 25)	3.2 ± 2.0	-	9/25 (36%)	-
B-RA-TKA (n = 25)	0.9 ± 0.7	-0.5 (-1.1 to + 0.1)	0/25 (0%)	-12% (-32% to + 8%)
B-CTKA (n = 25)	1.4 ± 1.1	-	3/25 (12%)	-
FTC
A-RA-TKA (n = 25)	1.0 ± 0.9	-1.7 (-2.4 to -1.0)***	1/25 (4%)	-28% (-48% to -8%)*
A-CTKA (n = 25)	2.7 ± 1.7	-	8/25 (32%)	-
B-RA-TKA (n = 25)	0.8 ± 0.7	-0.4 (-1.0 to + 0.2)	0/25 (0%)	-8% (-28% to + 12%)
B-CTKA (n = 25)	1.2 ± 1.0	-	2/25 (8%)	-
LTC
A-RA-TKA (n = 25)	1.2 ± 1.0	-1.8 (-2.6 to -1.0)***	2/25 (8%)	-36% (-56% to -16%)**
A-CTKA (n = 25)	3.0 ± 2.2	-	9/25 (36%)	-
B-RA-TKA (n = 25)	1.0 ± 0.8	-0.7 (-1.3 to -0.1)*	1/25 (4%)	-12% (-32% to + 8%)
B-CTKA (n = 25)	1.7 ± 1.3	-	3/25 (12%)	-

*P < 0.05, **P < 0.017 (after Bonferroni correction), ***P < 0.001

^†Outlier comparison was conducted using the chi-square test, and RD represents the risk difference (Risk Difference)

Early clinical function

In terms of clinical function, robotic-assisted surgery has brought significant early and long-term benefits to junior doctors. Three days after the surgery, the KSS and ROM of the A-RA-TKA group were significantly better than those of the A-CTKA group (MD + 12.1 points; MD + 12.1°), and the VAS pain score was lower (MD -1.2 points). At one year after the surgery, this advantage still existed in KSS and ROM. For senior doctors, robotic assistance improved KSS at 3 days after the surgery and ROM at 1 year after the surgery. The key point is that, under robotic assistance, the clinical function results of junior doctors (A-RA-TKA) at each time point after the surgery were not significantly different from those of senior doctors (B-RA-TKA) (all comparisons P > 0.05, except for the 1-year VAS where there was a minor but significant statistical difference) (see Table 5).

Table 5.

Comparison of preoperative, 3-day postoperative and 1-year postoperative clinical function scores (x̅ ± s) and mean differences between groups (MD, 95% CI)

Indicators and time points	A-RA-TKA (n = 25)	A-CTKA (n = 25)	MD (95% CI) A-RA vs. A-CT	B-RA-TKA (n = 25)	B-CTKA (n = 25)	MD (95% CI) B-RA vs. B-CT	MD (95% CI) A-RA vs. B-RA
KSS (subscale)
Before surgery	47.2 ± 11.4	48.5 ± 10.1	-1.3 (-7.3 to + 4.7)	48.9 ± 10.6	47.8 ± 11.2	+ 1.1 (-4.9 to + 7.1)	-1.7 (-8.3 to + 4.9)
3 days after surgery	70.3 ± 6.2	58.2 ± 6.1	+ 12.1 (+ 8.7 to + 15.5)*	71.8 ± 5.5	64.5 ± 5.8	+ 7.3 (+ 4.1 to + 10.5)*	-1.5 (-4.9 to + 1.9)
1 year after surgery	89.8 ± 7.1	82.6 ± 7.2	+ 7.2 (+ 3.2 to + 11.2)*	90.3 ± 5.9	90.5 ± 6.8	-0.2 (-3.8 to + 3.4)	-0.5 (-4.3 to + 3.3)
VAS (points)
Before surgery	6.7 ± 1.6	6.6 ± 1.4	+ 0.1 (-0.7 to + 0.9)	6.5 ± 1.3	6.8 ± 1.5	-0.3 (-1.1 to + 0.5)	+ 0.2 (-0.6 to + 1.0)
3 days after surgery	3.3 ± 1.3	4.5 ± 1.2	-1.2 (-1.9 to -0.5)*	3.1 ± 1.7	3.6 ± 1.0	-0.5 (-1.3 to + 0.3)	+ 0.2 (-0.6 to + 1.0)
1 year after surgery	0.8 ± 0.5	1.0 ± 0.3	-0.2 (-0.4 to 0.0)*	0.6 ± 0.3	0.7 ± 0.4	-0.1 (-0.3 to + 0.1)	+ 0.2 (0.0 to + 0.4)*
ROM (°)
Before the operation	101.6 ± 15.1	102.8 ± 14.5	-1.2 (-9.4 to + 7.0)	103.5 ± 14.2	100.2 ± 15.8	+ 3.3 (-5.1 to + 11.7)	-1.9 (-10.7 to + 6.9)
3 days after the operation	96.6 ± 8.1	84.5 ± 8.3	+ 12.1 (+ 7.5 to + 16.7)*	97.2 ± 7.5	95.8 ± 7.9	+ 1.4 (-3.0 to + 5.8)	-0.6 (-5.4 to + 4.2)
One year after the operation	126.7 ± 4.1	121.8 ± 4.2	+ 4.9 (+ 2.5 to + 7.3)*	128.9 ± 3.5	125.5 ± 3.9	+ 3.4 (+ 1.2 to + 5.6)*	-2.2 (-4.6 to + 0.2)

*P < 0.05, ***P < 0.001. MD: Mean Difference

Complications of the two groups of patients

Both groups of patients had no severe complications such as acute infection or vascular nerve injury; postoperative complications mainly included anterior femoral cortical notch (NOTCH), local exudation at the incision site, and intermuscular venous thrombosis in the affected limb. In both groups, the RTKA group had 1 case each of the above complications, while in the CTKA group, Group A had 3 cases of NOTCH, 1 case of local exudation at the incision site, and 1 case of intermuscular venous thrombosis in the affected limb; in Group B, the CTKA group had 1 case of NOTCH, 1 case of local exudation at the incision site, and 1 case of intermuscular venous thrombosis in the affected limb. The number of lower extremity thrombosis events was 1 in each of the four groups (P = 1.00). There was no statistically significant difference in the overall complication rate (P > 0.05), but the incidence of anterior femoral cortical notch in Group A of the CTKA group was higher.

Discussion

Homogenization of accuracy: originating from objective visual guidance of the robot

The A-RA-TKA group and the B-RA-TKA group showed no statistically significant differences in all dimensions of prosthesis alignment. This supports the characteristic of RA-TKA that it can reduce the experience differences through “objective guidance”. The data in Table 3 of this study indicates that in the hands of junior doctors, the A-RA-TKA group significantly outperformed the traditional TKA in multiple imaging alignment indicators such as HKA, FFC, FTC, and LTC. While in the hands of senior doctors, the precision of the RA-TKA procedure was only superior in the HKA angle. This suggests that RA-TKA can narrow the precision differences between senior and junior doctors in surgeries. Multiple studies have provided evidence for this: Kayani et al. [6] reported that the accuracy of prosthesis position remained stable from the first case when using the robotic arm system; Savov et al. [4]’s research also pointed out that there is no learning curve in implant positioning without image-based robot assistance, and the intraoperative planning implementation accuracy can reach < 2°. Bosco et al. [7]’s research also indicated that without an image navigation system, rapid learning can be achieved, and the precision remains at a high level throughout the process. The fundamental mechanism lies in that RA-TKA converts the freedom of relying on the surgeon’s spatial sense and “feel” in traditional surgeries into standardized operations within the visual virtual boundaries through digital three-dimensional planning and intraoperative active constraints/navigation. This provides a “high starting point operation platform” for young physicians, helping to reduce spatial judgment errors due to insufficient experience and making their technical output’s “starting point” closer to the “steady state” of senior physicians on the same platform.

Efficiency optimization: independence of personal experience and robot efficiency

Some previous literature has shown that RA-TKA has a clear efficiency learning curve [6, 8, 9]. Kayani et al. [6]’s study systematically evaluated the operation time, surgical team comfort, implant positioning accuracy, limb alignment, and postoperative complications through 60 consecutive traditional TKA cases and then one surgeon performing 60 consecutive RA-TKA cases. The results showed that 7 cases completed the learning curve, and the surgical accuracy remained reliable during the learning process. Savov et al. [4] and Kayani et al. [6] were consistent. One senior joint surgeon completed 70 consecutive TKA cases without image-based robot assistance, and the results showed that about 11 cases completed the learning curve, and the surgical accuracy was also maintained reliably during the learning process. Putzer et al. [9]’s study showed that the time for the 11th to 15th surgeries was significantly shorter than that for the 1st to 5th surgeries, and the doctor’s anxiety also significantly decreased. Two reports from Honghu system in China [8, 10] indicated that approximately 7–13 cases were needed to reach the learning curve inflection point; while Neira [11] used the ROSA system and reported that the learning curve extended to 43–61 cases, and it was positively correlated with the surgeon’s previous traditional TKA experience. Moreover, after mastering the ROSA system, the operation time would be shortened to be the same as that of senior doctors’ traditional TKA. This study assigned two doctors of different seniority levels to perform RA-TKA surgeries. Table 2 shows that overall, the operation time of RA-TKA was significantly longer than that of traditional TKA (P < 0.05), but there was no difference in “robot-specific time” between the senior and junior doctors; in traditional TKA, the operation time of junior doctors was significantly higher than that of senior doctors (P < 0.001); while in RA-TKA, there was no difference in the total operation time between the senior and junior doctors. This suggests that traditional TKA experience does not necessarily affect the robot operation speed of RA-TKA. Furthermore, previous studies have shown that RATKA, when dealing with complex cases, can make decisions through visual guidance, avoiding repeated adjustments and saving surgical time for operations such as positioning, navigation, and system evaluation, thus making the operation time consistent with that of traditional TKA. In this study, the operation time of RATKA in all groups was longer than that of traditional TKA, which might be related to the relatively simple cases selected in this study. Due to the retrospective nature of the study, neither of the two doctors was able to complete the cases consecutively within a short period of time, thus failing to complete an accurate learning curve assessment.

From individuals to systems: strengthened team collaboration dependence

The research by Brian et al. pointed out that the experience of assistants is a key variable affecting the efficiency of robotic surgery, and it can even extend the average robot surgery time by 26.9 min [5]. This is combined with the design of fixed skilled assistants in this study, revealing another key dimension for the success of RA-TKA: the team learning curve. The robot technology reduces the requirement for the individual skills of the surgeon while enhancing the reliance on standardized processes, skilled assistants, and team coordination. This means that the bottleneck of surgical efficiency shifts from “the surgeon’s hand” more towards “team collaboration”. In the fixed team condition of this study, the total operation time of RA-TKA was longer than that of traditional surgery, which reflects the complexity requirements of the overall surgical process for this new technology. Additionally, this study found that the incision of RA-TKA was prolonged, which was related to the need for extending the proximal incision for femoral condyle implantation with markers. Whether this operation can be avoided through team collaboration remains to be explored. Therefore, the promotion of RA-TKA should not only be the technical training of the surgeon but also the systematic team training and integration including assistants and nurses [5]. Only when the entire team collectively surmounts the learning curve can the precision and potential efficiency benefits of this technology be maximally released.

Clinical translation and long-term outlook

In the low-experience doctor group in this study, patients who received RA-TKA had better functional scores and pain relief at multiple time points after surgery than those who received traditional TKA (P < 0.05), and were close to the level of the high-experience doctor group (P > 0.05), suggesting that RA-TKA can narrow the gap in early rehabilitation effects between low-experience doctors and high-experience doctors. However, the sample size of this study was small, and the patients before surgery did not undergo standardized balance, so more accurate and reliable evaluations still require a larger sample size. Abnormal force lines and prosthesis positions are core risk factors for long-term aseptic loosening, accelerated wear, and revision [12–14]. Young physicians can achieve “high precision” and “operational stability” through RA-TKA, and the greatest clinical value lies in the expected improvement of long-term prosthesis survival rate and reduction of revision rate. It provides a technical-level standardized solution to address the issue of inconsistent efficacy caused by differences in surgeon experience or even operational habits, and provides a path for achieving uniform quality in surgical medical care. This awaits confirmation in longer-term follow-up studies [14].

Limitations and future prospects

This study has several limitations that should be considered when interpreting the results. Firstly, this study is a single-center retrospective analysis with a relatively small sample size. Although we controlled confounding factors to the greatest extent through strict inclusion criteria, unified perioperative management, and fixed surgical teams, this design still may limit the generalizability of the study conclusions to different medical environments and patient populations. Future studies need to conduct multi-center, large-sample prospective cohort studies to verify the broad applicability of this conclusion. Secondly, this study only evaluated one specific robot-assisted system (and Huahurwa). Different robot systems vary in their operational principles (such as image-dependent vs. image-independent) and human-computer interaction modes (active constraint style vs. navigation style), and their learning curves and accuracy improvement effects may not be the same. Therefore, our findings are mainly applicable to the initial application evaluation of similar technical platforms. Future research that directly compares the learning benefits of different systems for junior and senior doctors will be able to provide more universal high-level evidence for medical institutions’ technology selection. Finally, the follow-up period of this study is concentrated on 1 year after surgery, mainly evaluating surgical accuracy, early functional recovery, and short-term complications. Although a large number of literatures have confirmed that postoperative force line and prosthesis alignment accuracy are key factors affecting the long-term survival rate of total knee arthroplasty (TKA), whether robot-assisted surgery can ultimately lead to a lower long-term revision rate, better long-term function, and higher patient satisfaction still needs to be confirmed through longer follow-up. Tracking the long-term outcomes of this study cohort will be the focus of the next step of work.

Conclusion

This study, through comparative analysis, found that after receiving systematic short-term specialized training, junior surgeons in the field of surgery could significantly improve the accuracy of robotic-assisted total knee arthroplasty. This reduced the traditional technical gap between them and senior surgeons. The fundamental reason for this phenomenon lies in the inherent “objective visual guidance” mechanism of the robot-assisted system. This mechanism, through high-precision three-dimensional preoperative planning, real-time tracking and spatial positioning of bone structures during the operation, as well as intuitive quantitative feedback interfaces, transforms complex surgical operations into visual, controllable, and quantifiable precise steps, reducing the reliance on the surgeon’s experience and hand-eye coordination skills. It provides a stable and reliable technical empowerment platform for surgeons of different experience levels, promoting the standardization and homogeneity of surgical quality.

Author contributions

Song Xunzhou and Wang Mingyou jointly wrote the main manuscript text. Tang Zhuodong, Wang Hongping were responsible for data collection, image organization, patient follow-up, and manuscript revision. Lan Yuping was responsible for project supervision and manuscript revision.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.