探讨国产单孔机器人辅助妇科手术的学习曲线特征、影响因素及优化策略，分析不同术式、不同术者的学习曲线拐点。

采用前瞻性、多中心、单臂临床试验设计，于2023年1月至2025年10月在全国6家医院纳入97例采用术锐SR-ENS-600系统行机器人辅助妇科手术的患者，包括卵巢囊肿剔除(32例)、肌瘤剔除(11例)、子宫切除(45例)及子宫内膜癌分期(9例)四种术式。统计对接时间并与文献比较，采用累积和(cumulative sum，CUSUM)方法分析遥操作时间变化趋势，识别熟练度拐点。

本研究中内膜癌分期患者中位年龄低于文献报道(45岁vs. 61岁)，肌瘤剔除患者年龄高于文献报道(42.27岁vs. 36.98岁)。设备对接时间学习曲线较短，各医院中位对接时间为3.00~6.00 min，80.4%的病例在5 min内完成对接。不同医院间遥操作学习曲线差异显著：医院1呈现"下降-反弹-再下降"的多模态特征，约25例后进入熟练期；医院5首例即达熟练水平但后续进步有限；医院6呈现最典型学习曲线，于第5~6例进入稳定平台。不同术式学习曲线拐点也存在显著异质性：卵巢囊肿剔除术很快达到熟练；子宫切除术需22例方可达到稳定平台(中位遥操作时间105 min)。

国产单孔机器人妇科手术学习曲线存在显著的术式依赖性和个体异质性。高难度术式需20例以上学习周期，常规术式可在5例内达到稳定。术者既往单孔腹腔镜及多孔机器人经验可显著缩短单孔学习周期。国产术锐SR-ENS-600系统在对接时间、操作时间等指标上与达芬奇SP系统相当，具备良好临床可及性。推广双控制台教学及增强现实辅助反馈技术可帮助优化学习曲线。

To investigate the learning curve characteristics, influencing factors, and optimization strategies of domestic single-port robotic assisted gynecological surgery, and to analyze proficiency inflection points under different surgical procedures and surgeon backgrounds.

A prospective, multicenter, single-arm clinical trial was conducted at six hospitals across China between Jan. 2023 and Oct. 2025. A total of 97 patients undergoing robotic assisted gynecological surgery using the Shurui SR-ENS-600 system were enrolled, including 32 cases of ovarian cystectomy, 11 cases of myomectomy, 45 cases of hysterectomy, and 9 cases of endometrial cancer staging. Docking time was recorded and compared with literature data. The cumulative sum (CUSUM) method was used to analyze the trend of console time and to identify proficiency inflection points.

In this study, the median age of patients undergoing endometrial cancer staging was lower than that reported in the literature (45 years vs. 61 years), while the age of myomectomy group was higher (42.27 years vs. 36.98 years). The learning curve for docking time was short, with median docking times ranging from 3.00 to 6.00 minutes across hospitals, and 80.4% of cases completed docking within 5 minutes. Significant inter-hospital differences in the learning curves of console time were observed: hospital 1 exhibited a "decline-rebound-decline" pattern, entering the proficiency phase after approximately 25 cases; hospital 5 achieved proficiency from the first case but showed limited subsequent improvement; hospital 6 demonstrated the most typical learning curve, reaching a stable plateau at 5-6 cases. Significant heterogeneity in learning curve inflection points was also found across different surgical procedures: routine procedures (Type 1) achieved proficiency quickly, while difficult procedures (Type 3) required 22 cases to reach a stable plateau (median console time of 105 minutes).

The learning curve for domestic single-port robotic gynecological surgery exhibits significant procedure-dependency and inter-individual heterogeneity. Difficult procedures require more than 20 cases to achieve proficiency, while routine procedures can reach a stable level within 5 cases. Surgeons′ prior experience in single-port laparoscopy and multi-port robotic surgery can significantly shorten the learning cycle for single-port robotic surgery. The domestic Shurui SR-ENS-600 system demonstrates comparable docking time and operative time to the da Vinci SP system, indicating good clinical accessibility and potential for widespread adoption. Dual-console teaching and augmented reality-assisted feedback techniques can help optimize the learning curve.